BIOACCUMULATION OF TRACE ELEMENTS BY BIVALVES IN THE ALTAMAHA

RIVER SYSTEM

by

W. AARON SHOULTS-WILSON

(Under the Direction of Marsha C. Black)

ABSTRACT

Trace elements occur naturally within the Earth’s crust but human activity can introduce

them into aquatic environments, resulting in elevated concentrations, potentially leading to

toxicity. The purpose of this dissertation was to evaluate a biomonitor approach for assessing

trace elements in the Altamaha River system. First, bioaccumulation of trace elements in the

Asian clam, Corbicula fluminea, was compared to that of the co-occurring native mussel, Elliptio

hopetonensis. Corbicula was shown to accumulate higher concentrations of several potentially

toxic trace elements (As, Cd, Cu and Hg) than E. hopetonensis. When compared across sites,

concentrations of As, Cd, Cu, Hg and Pb were correlated between the two species, supporting

Corbicula’s suitability as an indicator of concentrations in E. hopetonensis. A wider survey of

bioaccumulation using only Corbicula found significant sources of Cd, Cr, Cu, Hg and Zn within

the Altamaha system. Controlling for natural variation in environmental and individual

parameters eliminated some statistical significance in this survey. Site location in the watershed,

dissolved oxygen, sediment composition, concentrations of elements in the sediment and

organism length and condition were factors that influenced trace element bioaccumulation.

Finally, E. hopetonensis shells were sectioned and the trace element profiles of annuli were

analyzed using laser ablation inductively coupled plasma-mass spectrometry (LA ICP-MS). Mn

was found to accumulate in the shell in a seasonal manner. Using Mn profiles to compare shells

from the same site aligned peaks in Pb concentration were between shells. This indicates that

freshwater bivalve shells could serve as archives of the local environment. Overall, these

findings elucidate some of the aspects needed for a successful and informative biomonitoring

program for trace metals using freshwater bivalves, as well as suggesting some future methods of

biomonitoring.

INDEX WORDS: Altamaha River, biomonitor, bioaccumulation, Corbicula fluminea,

arsenic, cadmium, copper, mercury, lead, zinc, laser ablation, freshwater mussel, sediment, water chemistry.

BIOACCUMULATION OF TRACE ELEMENTS BY BIVALVES IN THE ALTAMAHA

RIVER SYSTEM

by

W. AARON SHOULTS-WILSON

BS Chemistry, Truman State University, 2003

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2008

© 2008

W. Aaron Shoults-Wilson

All Rights Reserved

BIOACCUMULATION OF TRACE ELEMENTS BY BIVALVES IN THE ALTAMAHA

RIVER SYSTEM

by

W. AARON SHOULTS-WILSON

Major Professor: Marsha C. Black

Committee: Alan Covich Raymond Noblet Jay Overmyer

Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia December 2008

iv

DEDICATION

In loving acknowledgement of some of my greatest teachers, my grandparents: June

Meece (botany); Russell Meece (land management); Bill Wilson (zoology); Mary Wilson

(music).

v

ACKNOWLEDGEMENTS

First, I would like to thank my committee: Marsha Black, Alan Covich, Ray Noblet and

Jay Overmyer, for overseeing the evolution of this dissertation and for continually giving me

new ways to look at what I was doing. Especial thanks go to my advisor, Marsha Black, for her

unflagging support and optimism. From my first day here, she has provided me with the advice,

encouragement and direction that I needed. Jason Unrine deserves many thanks as a long-

serving committee member and for providing access to the instruments and expertise needed to

analyze my samples. Lynne Seymour and Jim Peterson patiently guided me through the

statistical methods that made this work possible. Jason Wiskniewski from GA DNR taught me

how to identify the mussels of the Altamaha and counted shell annuli. Jimmy Rickard at Fish &

Wildlife helped me to focus this study and helped me access sites via boat. A host of other

people also provided assistance with sampling: Jason Meador and Becky Fauver let me ride

along on their sampling trips and helped me with my collections; Miles Buzbee, Scott Small,

Keith Hastie, Marsha Black, Jimmy Rickard, and Jeff Turner all helped me with sampling on

various occasions. I am grateful for the support of my labmates: Suzy Baird, Deanna Conners,

Brad Konwick and Emily Rogers; as well as the rest of my friends at EHS: Carrie Futch, Bradd

Haley, Arena Richardson and Jeff Turner. My family is one of the central supports of my life,

especially my grandmother Mary Wilson, my sister Sierra Wilson and my parents, Randy and

Melissa Wilson, who first encouraged me to pursue a career in science. And then there is my

wife Rhiannon, who was with me every step of the way on this project and always helped me

remain positive. Financial support for this research came from the Fish & Wildlife Service and

vi

the University of Georgia via stipends and small equipment grants from the Interdisciplinary

Toxicology Program. I am also grateful to the University of Georgia Graduate School, for

providing support to me through a Presidential Fellowship.

vii

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS.............................................................................................................v

LIST OF TABLES...........................................................................................................................x

LIST OF FIGURES ...................................................................................................................... xii

CHAPTER

1 INTRODUCTION .........................................................................................................1

References .................................................................................................................4

2 FRESHWATER MUSSELS (FAMILY UNIONIDAE), ASIAN CLAMS

(CORBICULA FLUMINEA), AND BIVALVE (CLASS BIVALVIA) SHELLS AS

BIOMONITORS OF TRACE ELEMENT CONTAMINATION ............................6

Abstract .....................................................................................................................6

Introduction ...............................................................................................................7

Overview of Exposure Routes and Bioaccumulation of Trace Elements in Bivalves

.............................................................................................................................9

Tissue Bioaccumulation of Trace Elements by Freshwater Bivalves .....................11

Bioaccumulation of Trace Elements in the Shell Annuli of Bivalves.....................21

Conclusions .............................................................................................................30

References ...............................................................................................................34

3 BIOACCUMULATION OF TRACE ELEMENTS BY CORBICULA FLUMINEA

AND ELLIPTIO HOPETONENSIS IN THE ALTAMAHA RIVER SYSTEM.....45

viii

Abstract ...................................................................................................................45

Introduction .............................................................................................................46

Materials and Methods ............................................................................................48

Results .....................................................................................................................52

Discussion ...............................................................................................................54

Conclusions .............................................................................................................59

Acknowledgments ...................................................................................................60

References ...............................................................................................................69

4 THE ASIAN CLAM CORBICULA FLUMINEA AS A BIOMONITOR OF TRACE

ELEMENT CONTAMINATION: ACCOUNTING FOR NATURAL

VARIATION WHEN IDENTIFYING ANTHROPOGENIC SOURCES .............75

Abstract ...................................................................................................................75

Introduction .............................................................................................................76

Materials and Methods ............................................................................................78

Results .....................................................................................................................84

Discussion ...............................................................................................................85

Conclusions .............................................................................................................93

Acknowledgments ...................................................................................................94

References .............................................................................................................103

5 USE OF AUTOREGRESSION TO ANALYZE LASER ABLATION

INDUCTIVELY COUPLED PLASMA-MASS SPECTROSCOPY DATA FROM

BIVALVE SHELLS..............................................................................................112

Abstract .................................................................................................................112

ix

Introduction ...........................................................................................................113

Materials and Methods ..........................................................................................115

Results ...................................................................................................................118

Discussion .............................................................................................................120

Conclusions ...........................................................................................................125

Acknowledgments .................................................................................................126

References .............................................................................................................139

6 CONCLUDING REMARKS.....................................................................................145

x

LIST OF TABLES

Page

Table 2.1: A table summarizing the criteria of good biomonitors, how closely they are met by

freshwater bivalves, with references relating to the appropriate point ...........................32

Table 2.2: Field studies that have analyzed intra- and/or inter-annual variability in trace element

concentrations of bivalve shells. .....................................................................................33

Table 3.1: Mean concentration of each element as it was accumulated by each species over all

sites .................................................................................................................................61

Table 3.2: A list of physical and environmental parameters significantly (p < 0.05) correlated to

the mean amounts of each element at each site as calculated by tissue concentration

(μg/g) or body burden (g) ...............................................................................................62

Appendix 3.1: Element concentrations (in μg/g) in the tissues of each bivalve species, given as

the mean of each site, plus or minus standard error........................................................73

Table 4.1: Best fitting models for each metal, with the model’s parameters, the number of

parameters (K), the residual sum of squares (RSS), the Aikake Information Criteria

(AICC), difference between the AIC of the model and that of the model with the lowest

AIC (Δi), and the Aikake weight (wi) .............................................................................95

Table 4.2: Selected linear hierarchical models for each trace element, with the predictors for each

model and percent decrease of τ00 (intrer-site variability) and σ2 (intra-site variability)96

Appendix 4.1: Concentrations (ppm, μg/g) of elements at each site in three compartments: water,

sediment and Corbicula tissues ....................................................................................110

xi

Table 5.1: Mean concentrations of elements (μg/g of Ca43), size parameters (shell length, height,

depth and shell weight) and estimated number of annuli ablated (i.e. number of years

the trace element data represents) of individual shells analyzed in this study. Average

values for each site are given in bold............................................................................127

Table 5.2: Correlations between Mn data series for shells within a given site, at the lags selected

for greatest average correlation.....................................................................................128

Table 5.3: The fit of the least squares lines for the Mn data series, calculated for a linear

regression (Lin) and a linear autoreggression (Auto) ...................................................129

Table 5.4: Autoregression models for Mn data series, including number of cyclical components,

% of cyclical components that are significant (α = 0.10) and MSE for both the least

squares (LS) model and the least squares model with cyclical components (LSCC)...130

Appendix 5.1: Correlations between different elements within the same shell...........................143

Appendix 5.2: The correlation between Cd, Cu, Pb and Zn from different shells at the lags

determined by correlations between Mn data series .....................................................144

xii

LIST OF FIGURES

Page

Figure 3.1: A map of sites sampled within the Altamaha River system........................................64

Figure 3.2: The natural log of mean metal(loid) concentration in the tissue of E. hopetonensis at

each site, plotted as a function of the natural log mean concentration in the tissue of

Corbicula.......................................................................................................................65

Figure 3.3: The natural log of mean metal(loid) body burden of E. hopetonensis at each site,

plotted as a function of the natural log mean body burden of Corbicula......................66

Figure 3.4: Mean tissue concentrations found in Corbicula and E. hopetonensis of A) As and Cd;

B) Cu and Zn; C) Hg and Pb at each site, arranged up to downstream.........................67

Figure 4.1: Map of the locations sampled in this study .................................................................98

Figure 4.2: Average Corbicula tissue concentrations of (A) Cu and Zn; (B) As, Cd and Cr; and

(C) Hg and Pb of clams found upstream and downstream of sites of interest: Amercord

tires (AMC), Little Commissioner’s Creek (LCC), Kettle Creek (KC), Plant Hatch

(PH), Rayonier (RAY), and Reidsville State Penitentiary (RSP) .................................99

Figure 4.3: Differences in mean Corbicula tissue concentrations of (A) Cu and Zn; (B) As, Cd

and Cr; and (C) Hg and Pb between clams found upstream and downstream of sites of

interest: Amercord tires (AMC), Little Commissioner’s Creek (LCC), Kettle Creek

(KC), Plant Hatch (PH), Rayonier (RAY), and Reidsville State Penitentiary (RSP) .101

Figure 5.1: Sampling locations on the Oconee (OCN) and Altamaha (ALT) Rivers..................132

Figure 5.2: Mn data series, offset by lags of highest overall concentration ................................133

xiii

Figure 5.3: Regressions (black lines) of mean Mn series (gray lines).........................................135

Figure 5.4: Mean Pb series (black line), offset by the same lags as the mean Mn series ............137

1

CHAPTER ONE

INTRODUCTION

The course of evolutionary and human history has rendered potentially toxic trace

elements such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), lead

(Pb) and zinc (Zn) among the hardest contaminants to monitor, mitigate and regulate. Unlike

organic xenobiotic contaminants (e.g. pesticides, pharmaceuticals, etc.) potentially toxic trace

elements occur naturally within the earth’s crust. With xenobiotic pollutants, the ultimate source

of contaminants is rarely in question: human beings manufactured these molecules and human

activity released them into the environment. On the other hand, certain mineral and soil

formations may carry naturally high levels of trace elements. These may leach into local streams

and wetlands and be taken up by plants and animals, masking the additions caused by human

activities.

Over time xenobiotic molecules decay, being metabolized by higher organisms or

degraded by microorganisms into smaller components. Trace elements do not degrade in the

same manner. Rather, they are passed from one environmental compartment to another, used as

catalytic centers by biomolecules, sequestered in soils and bioinorganic structures, transformed

into oxides, sulfides and nitrates and carried on air and waterborne particles: they remain

indestructible. Therefore, the pollution of the past continues to haunt us in the present. The

large amounts of trace elements belched up by the smokestacks of the industrial revolution leave

records in tree rings, peat bogs and stratified layers of sediment (Shotyk et al. 2003, Heim et al.

2004, Abreu et al. 2008). Attributing trace elements to current human activity must contend with

2

the distribution of elements of the geologic past, as well as all the redistributions during human

history.

Furthermore, xenobiotics are new chemicals. While their properties may be similar to

those of natural molecules, chemical alterations make them more likely to act as toxicants by

interacting with biochemical pathways in expected or unexpected manners. With trace elements,

the route between exposure and toxicity is less direct. Exposure throughout evolutionary history

has resulted in many trace elements (e.g. Ca, Cu, Fe, Mg, Mn, Se, Zn, etc) becoming a vital part

of the cellular machinery of living organisms (Silva and Williams 2001). These essential

elements are carefully regulated by biochemical mechanisms of uptake, sequestration,

accumulation, utilization and elimination in order to maintain ideal concentrations within the

tissues of organisms (Newman 1998). In fact, in some instance, trace element deficiency in an

environment can be as much or more detrimental to organisms as increased exposure.

A separate class of trace elements has no known biological function (e.g. Cd, Hg, Pb).

These non-essential elements can cause toxicity at low levels by disrupting biochemical

pathways and tend to accumulate to higher levels than essential elements because of relatively

inefficient biochemical detoxification and elimination pathways (Silva and Williams 2001).

With the notable exceptions of the non-essential elements, most trace elements are not inherently

toxic. However, for many of them (e.g. Ag, Al, As, Cr, Cu, Ni, Se, Sn, Zn) the potential for

toxicity is there, if organisms are exposed to higher levels than they are able to cope with.

Therefore, human activities that increase the levels of these trace elements in the

environment have the potential to adversely affect all exposed organisms. Currently, human

sources constitute large percentages of the trace elements that enter environmental compartments

(Callender 2005). Human activities have therefore caused a redistribution of trace elements on a

3

global scale, introducing them into systems where they would naturally exist in small amounts

and making them more readily available to organisms by extracting them from normally

inaccessible minerals.

In the southeastern United States, industry and urbanization, intensive cotton cultivation

and clear cutting have all led to vast shifts in environmental structure in the last two centuries

(Merchant 2007). One particular adverse effect of these environmental changes is the

endangerment of the world’s most diverse assemblage of freshwater mussel species (Williams et

al. 1993, Neves 1999, Strayer et al. 2004). As a hotspot for biodiversity, the southeast also holds

a large proportion of the nation’s endangered or threatened mussel species (Williams et al. 1993).

Even as conservation organizations attempt to preserve these species, the extreme drought of

2007 has demonstrated the difficulty in balancing human demands with those of endangered

species (AP 2007).

At the crossroads of biodiversity preservation and monitoring of contaminants lies

biomonitoring, an attempt to use living organisms as sensors of contamination. Using

biomonitors allows investigators to identify increases of contaminants in a biologically relevant

way by monitoring an organism’s interaction with this contaminant. This is especially important

for trace elements, when interactions between contaminant and organism can be subtle and

complex. Freshwater bivalves, including both native and invasive species, have long been

considered compelling candidates to be used as biomonitor organisms (Doherty 1990, Elder

1991). They could prove especially compelling monitors of pollution in the same areas that hold

threatened mussel species, because the physiology is bound to be more similar between two

bivalve species than between two less similar organisms.

4

In this dissertation, I apply the principles of biomonitoring using freshwater bivalves in

the Altamaha River, a Georgia watershed serving as habitat for threatened endemic freshwater

mussel species (Meador 2008). Chapter Two reviews some of the recent literature dealing with

the bioaccumulation of trace elements in freshwater mussels in general and in particular the

invasive Asian clam Corbicula fluminea. Special attention is given to the use of bivalve shells as

archival records of the environment and trace element exposure. In Chapter Three, the use of

Corbicula bioaccumulation as an indication of trace element contamination is compared to

bioaccumulation in a co-occurring native freshwater mussel (Elliptio hopetonensis). Species-

specific differences in tissue concentrations of trace elements, inter-site correlations between the

two species, spatial distribution of elements and potential confounding factors are analyzed and

discussed. Sampling over a wider area in Chapter Four, a method using bioaccumulation of trace

elements in Corbicula to analyze sources of contamination in the Altamaha River watershed is

investigated. Linear hierarchical models are used to remove naturally occurring variation in

tissue concentrations of trace elements. Finally, in Chapter Five, trace metals in the shells of the

freshwater mussel E. hopetonensis are analyzed as archival records of environmental conditions

and exposure to potentially toxic trace elements.

References

Abreu, S. N., A. M. V. M. Soares, A. J. A. Nogueira, and F. Morgado. 2008. Tree rings, Populus nigra L., as mercury data logger in aquatic environments: Case study of an historically contaminated environment. Bulletin of Environmental Contamination and Toxicology 80:294-299.

AP. 2007. Georgia's governor declares drought emergency. http://www.msnbc.msn.com/id/21393296/. Associated Press.

Callender, E. 2005. Heavy metals in the environment--historical trends in environmental chemistry, Lollar B.S. ed. 1 edition. Elsevier, Amsterdam, The Netherlands.

5

Doherty, F. G. 1990. The Asiatic clam, Corbicula Spp, as a biological monitor in freshwater environments. Environmental Monitoring and Assessment 15:143-181.

Elder, J., Collins, JJ. 1991. Freshwater molluscs as indicators of bioavailability and toxicity of metals in surface-water systems. . Reviews Environ Contam Tox 122:37-79.

Heim, S., J. Schwaubauer, A. Kronimus, R. Littke, C. Woda, and A. Mangini. 2004. Geochronology of anthropogenic pollutants in riparian wetland sediments of the Lippe River (Germany). Organic Geochemistry 35:1409-1425.

Meador, J. R. 2008. The development and evaluation of a freshwater mussel sampling protocol for a large lowland river. University of Georgia, Athens, GA.

Merchant, C. 2007. American environmental history : an introduction. Columbia University Press, New York.

Neves, R. J. 1999. Conservation and commerce: management of freshwater mussel (Bivalvia : Unionoidea) resources in the United States. Malacologia 41:461-474.

Newman, M. C. 1998. Fundamentals of ecotoxicology. Ann Arbor Press, Chelsea, MI.

Shotyk, W., M. E. Goodsite, F. Roos-Barraclough, R. Frei, J. Heinemeier, G. Asmund, C. Lohse, and T. S. Hansen. 2003. Anthropogenic contributions to atmospheric Hg, Pb and As accumulation recorded by peat cores from southern Greenland and Denmark dated using the 14C "bomb pulse curve". Geochimica Et Cosmochimica Acta 67:3991-4011.

Silva, J. J. R. F. d. and R. J. P. Williams. 2001. The biological chemistry of the elements : The inorganic chemistry of life. 2nd edition. Oxford University Press, Oxford ; New York.

Strayer, D. L., J. A. Downing, W. R. Haag, T. L. King, J. B. Layzer, T. J. Newton, and S. J. Nichols. 2004. Changing perspectives on pearly mussels, North America's most imperiled animals. Bioscience 54:429-439.

Williams, J. D., M. L. Warren, K. S. Cummings, J. L. Harris, and R. J. Neves. 1993. Conservation status of freshwater mussels of the United States and Canada. Fisheries 18:6-22.

6

CHAPTER TWO

FRESHWATER MUSSELS (FAMILY UNIONIDAE), ASIAN CLAMS (CORBICULA

FLUMINEA), AND BIVALVE (CLASS BIVALVIA) SHELLS AS BIOMONITORS OF

TRACE ELEMENT CONTAMINATION1

Abstract

Freshwater bivalves have received widespread interest as biomonitor organisms of

contaminants. This paper reviews recent studies using freshwater bivalves in general and the

Asian clam Corbicula fluminea in particular as biomonitors of potentially toxic trace element

contamination. Individual growth and size, the effects of seasonal fluctuations in temperature

and reproductive state, water and sediment chemistry and the differing biological and chemical

characteristics of trace elements have been reported to affect how bivalves accumulate trace

elements. Fewer attempts have been made to definitively link trace element accumulation to

adverse effects at higher levels of biological organization. A new approach to biomonitoring

analyzes growth annuli in bivalve shells for archival records of trace element exposure. Further

research in the field should focus on 1) explaining the effects of confounding factors such as size

and season of collection and how much variation they explain compared to anthropogenic inputs;

2) linking bioaccumulation to adverse effects; and 3) exploring the use of bivalve shells as

archives of trace element exposure.

1 Shoults-Wilson WA. Portions to be submitted to Hydrobiologia.

7

Introduction

A biomonitor organism is a species that has been chosen to provide indications of the

potential impacts of anthropogenic activities on wild populations. Anthropogenic impacts on

natural communities are numerous and include alteration of habitat, direct harvest of certain

species, disruption of naturally occurring nutrient cycles and the introduction of increased

amounts of toxic elements or compounds. Typically, biomonitor organisms are employed to

determine the impacts of contaminants such as pesticides, other organic xenobiotic compounds

and potentially toxic trace elements that are released into a system due to human activities.

Several criteria must be considered in order to determine the suitability of candidate

biomonitor species based on Elder (1991): 1) tolerance to the contaminants in question; 2)

mobility, with less mobile organisms preferable; 3) a life span long enough to allow sampling of

multiple age-classes; 4) ubiquity and abundance at sites of interest; 5) ability to obtain enough

tissue for appropriate analyses; 6) ability to survive sampling and laboratory testing disturbances;

7) ease of identification and sampling; 8) display of a metric of exposure to the contaminant(s) of

interest; 9) a link between this metric and metrics of environmental health or special concern

(Table 2.1).

One group of organisms that have become common biomonitors in both marine

(Farrington 1983) and freshwater (Elder 1991) systems are bivalve species [class Bivalvia

(McMahon 2001)]. Bivalves are mollusks, which secrete a calcium carbonate shells in two

halves (or valves) with an external hinge. They are primarily filter feeders, drawing water in

through a siphon and removing suspended food particles, although they can use their muscular

“foot” to sweep food particles into their shell cavity (Reid et al. 1992, McMahon 2001).

8

The suitability of freshwater bivalves as biomonitors according to the criteria listed

previously is summarized in Table 2.1. Briefly, freshwater mussels are large, mostly sedentary

organisms, which burrow in the sediment of aquatic systems and move very little even during

storm surges (Imlay 1982, Schwalb and Pusch 2007). Mussels in the family Unionidae are

typically long lived (up to 20 years maximum age on average), while other freshwater bivalves

(such as clams of the family Corbiculidae) have shorter life spans (up to 3-4 years maximum

age) (Vaughn and Hakenkamp 2001). They are usually resistant to a wide array of contaminants

and can survive short-term disturbances, although these characteristics are species specific (Elder

1991). Freshwater mussels typically colonize aquatic habitat in areas of dense aggregation

known as beds, making abundance within a system extremely patchy (Strayer 1999, Morales et

al. 2006, Strayer 2008). While they can easily be sampled from wadeable portions of streams, it

can be difficult to distinguish certain species (Campbell et al. 2005). The final two criteria

(metric of exposure and correlation to environmental health) are the subject of most research on

the suitability of bivalve species as biomonitor organisms. Findings vary between studies,

species and contaminants of concern.

The purpose of this review is first to summarize research that has investigated the use of

freshwater bivalves as biomonitor of a diverse group of potentially toxic contaminants: trace

elements (e.g. As, Cd, Cr, Cu Hg, Pb, Zn, etc). Several reviews have previously been published

on trace metal accumulation in bivalves, including one focusing on all freshwater mollusks

(Elder 1991); another specific to the use of the invasive Asian clam Corbicula fluminea

(Doherty 1990); another specific to employing bivalve shells to monitor metals (Imlay 1982);

and more recently a mini-review devoted to the ecotoxicology of metals on freshwater mussels

(Naimo 1995). This review will include elements of all of these reviews and synthesize the

9

issues involving investigating the bioaccumulation of trace elements by freshwater mussels and

Corbicula fluminea (hereafter Corbicula) and the use of mussel shells as biomonitor surrogates.

This final section will be given special attention, with a focus on the analysis of growth annuli in

bivalve shells for trace element composition.

Overview of Exposure Routes and Bioaccumulation of Trace Elements in Bivalves

A bewildering array of natural processes define how trace elements move in the

environment and are eventually accumulated by aquatic organisms. As described in Foster and

Charlesworth (1996), trace elements entering fluvial systems are primarily sorbed to particulate

matter and sediment, especially very fine sediment. Trace metals in sediments can exist in a

variety of chemical states and complexes, which may be more or less available for uptake by

benthic organisms (Salomons et al. 1987, Tessier and Campbell 1987, Foster and Charlesworth

1996). Freshwater bivalves, as organisms that actively burrow in sediment and feed on detrital

deposits, are more likely than many other organisms to be exposed to trace elements bound to

sediments. For instance, Tessier et al. (1984) found that Cu, Pb and Zn bound to iron (Fe)

oxides, organic matter and sulfides were best predictive of accumulation in Elliptio complanata.

Applying a more rigorous approach, Tessier et al. (1993) developed a model for Cd

accumulation in Anodonta grandis that included Fe oxide, organic matter, pH and Cd

concentration in lake sediments as predictors. These biogeochemical models indicate that the

major factors affecting bioaccumulation are related to the chemistry of the surficial sediment that

provides bivalves with habitat.

Alternatively, the biodynamics concept has been proposed as a means of understanding

the effects of four primary factors on bioaccumulation: “metal specificity, environmental

influences, exposure route, and species-specific characteristics” (Luoma and Rainbow 2005).

10

This approach typically uses controlled, laboratory experiments to determine the kinetics of

uptake and loss of trace elements via various pathways. These results are used in the dynamic

multi-pathway bioaccumulation model (DYM-BAM) to predict the concentration of

contaminants in an organism at steady state (CSS):

CSS = [(ku * CW) + (AE * IR * CF)] / (ke + g)

Where ku is a constant of uptake, CW is the water concentration of the element in questions, AE

is a measure of assimilation efficiency from food for that element, IR is a rate of food intake, CF

is the concentration in food of the element of question, ke is a constant of elimination, and g is

the rate of growth of the organism (Luoma and Rainbow 2005).

The DYN-BAM model of accumulation has been applied to a wide variety of aquatic

invertebrates, as reviewed and synthesized by Rainbow (2002) and Wang & Fisher (1999). A

principle finding from these studies has been that metal accumulation is proportional to the

assimilation efficiency (AE) of metals from food (Wang and Fisher 1999). The implication of

this finding is that most bioaccumulation of metal occurs from exposure. This is not surprising,

considering that the concentrations of metals found in food resources, sediment and particulates

are usually orders of magnitude higher than aqueous concentrations.

Most of the biodynamic modeling to date has focused on marine species of bivalves such

as the blue mussel Mytilus edulis (Wang and Fisher 1999), the clam Macoma balthica (Griscom

et al. 2002) and the green mussel Perna viridis (Shi and Wang 2005). However, the same

conclusions appear to hold true for freshwater bivalves. For instance, Roditi & Fisher (1999)

reported that the freshwater zebra mussel, Dreissena polymorpha accumulate up to 100 times the

amount of the elements Ag, C, Cd, Hg and Se from food than water. A further experiment in the

11

field found that D. polymorpha accumulates most of its Ag, Cd and Hg from ingested particulate

matter and dissolved organic carbon (Roditi et al. 2000b, a).

Tissue Bioaccumulation of Trace Elements by Freshwater Bivalves:

The bioaccumulation of trace elements by freshwater mussels has been more

exhaustively reviewed in Elder and Collins (1991), while the use of Corbicula as a biomonitor

for trace elements was first reviewed by Doherty (1990). Therefore, this paper will review more

recent studies using freshwater bivalves (especially Corbicula) as biomonitors of trace elements,

with a focus on the issues involved in interpreting those results.

Tissue Specific Bioaccumulation

Bioaccumulation of trace metals has been shown to occur at different concentrations in

different tissues of freshwater mussels, although the distinct tissues vary from study to study.

For instance, Adams et al. (1981) found Cd and Zn concentrated in the gills of transplanted

Amblema perplicata when those tissues, along with foot, mantle viscera, were analyzed

separately. The zebra mussel D. polymorpha and a co-occurring mussel Mytilopsis leucophaeta

have been found to accumulate more Cd, Cu and Zn in the byssus (proteinaceous threads used to

attach to hard substrate) than the soft body tissue or shell (Van der Velde 1992). Gundacker

(2000) divided Unio pictorum and Anodonta sp. into viscera, gill, mantle, adductor muscle and

shell. The gills of both species had some of the highest concentrations of Cd, Cu, Pb and Zn,

while the shell tended to have the lowest concentrations of these elements.

Investigations into the tissue-specific accumulation of trace elements in Corbicula have

been limited and primarily investigated in a laboratory environment, probably due to the small

size of the organisms. In a study that combined water and sediment exposures, inorganic and

methylated Hg and Cd predominantly accumulated in the viscera and gills, rather than the

12

mantle, foot or kidney tissues (Inza et al. 1997). Similar results were obtained in another

laboratory study (Baudrimont et al. 1997) but with the gills showing higher accumulation of Cd

and Hg, followed by viscera and the other tissues. These studies both indicate that Corbicula

probably accumulates trace elements in a manner similar to freshwater mussels, with gills

accumulating higher concentrations, although mussel viscera have not been shown to have

significantly higher concentrations of Cd, Pb, or Zn (Adams et al. 1981, Gundacker 2000).

Conners et al. (1999) reported Pb concentrations in Corbicula shells up to 89% greater than that

of the adductor muscle and foot tissues. Such high concentrations in shells contradicts to the

findings of Gundacker (2000) who discerned no significant difference in Pb concentrations

between shells and adductor muscles in two freshwater mussels. This discrepancy may be

explained by the difference between controlled laboratory experiments (Sturresson 1976,

Almeida et al. 1998, Conners 1999) that have regularly reported Pb concentrations in bivalve

shells 1 to 2 orders of magnitudes higher than those found by field studies (Bourgoin and Risk

1987, Bourgoin 1990, Gundacker 2000). In other words, a controlled laboratory environment

introducing environmental media enriched in trace elements may influence the distribution of

those elements in the tissues of biomonitor organisms. One possible explanation is that the

coping mechanisms used by bivalves to survive acute doses of trace elements may differ from

those used in dealing with the chronic exposure that is more common in the natural environment.

Furthermore, it may be related to the preference in laboratory studies for aqueous exposure,

while most environmental accumulation is related to dietary exposure.

A little understood aspect of trace element bioaccumulation is the effect that the presence

of other trace elements has on mechanisms of accumulation. Studies into tissue-specific

distributions of trace elements have touched on this issue, which is undoubtedly a factor in the

13

environment where all trace elements naturally occur, although usually absent in most laboratory

studies which are often concerned with only a single element. A 1986 laboratory study (Cassini

et al.) found Cd exposure to increase Zn concentration in the gills of Anodonta cygnea but not

Unio elongatulus. Other studies suggest that rather than Cd affecting Zn accumulation, Zn exerts

an antagonistic effect on Cd accumulation, not surprising given that both elements favor a 2+

oxidation state and have a similar ionic radius. The conclusions of Hemelraad et al. (1987)

indicate that A. cygnea exposed to Zn as well as Cd accumulated lower concentrations of Cd

overall, specifically in the gills, with slightly higher levels of Cd accumulated in the midgut. A

more recent study reached similar conclusions for D. polymorpha and Corbicula, with Zn/Cd

mixtures causing the bivalves to accumulate lower concentrations of Cd than exposure to Cd-

only solution (Marie et al. 2006). These studies demonstrate just one of the many interactions

that may occur between trace elements in the environment.

Seasonal Differences in Accumulation

It is necessary to consider the season of collection when using freshwater bivalves as

monitors of trace elements. It is commonly assumed that seasonal changes in organism tissue

mass (caused by the reproductive cycle) and environmental concentrations of trace metals

(caused by high flow events) will result in different tissue concentrations of trace elements

(Abaychi and Mustafa 1988, Luoma et al. 1990). However Couillard et al. (1995b) found during

the course of a year-long transplant study using Pyganodon grandis that while the mussels’

condition (tissue dry wt / shell wt) changed in a seasonal manner, tissue concentrations of Cd and

Zn were more closely related to environmental concentrations of those metals. A study using the

Brazilian mussel Anodontites trapesialis found seasonal differences in the concentration of Cd

and Pb only at two sites (Tomazelli et al. 2003). Again, point source proximity was more

14

important in explaining Pb accumulation. Finally, while Angelo et al. (2007) found evidence of

seasonally variable water and fine sediment concentrations of Cd, Pb and Zn caused by high-

flow events, they concluded that these affected metal accumulation less than anthropogenic

inputs when compared across a wide area.

Seasonality has also been implicated as a factor influencing the accumulation of trace

elements in Corbicula. This is a reasonable assumption, since Corbicula only feed and grow for

a certain portion of the year when the water temperature is above 14˚ C (Joy 1985). Studies in

artificial streams have shown increased accumulation of Cd with increased temperature (Graney

et al. 1984). Biodynamic models for Corbicula indicate that it accumulates Cu primarily from

dietary sources (Croteau et al. 2004, Croteau and Luoma 2005). Therefore the biodynamic

theory predicts seasonal differences in accumulation based on metabolic activity and sources of

food, such as seasonal algal blooms.

Actual field verification of seasonality in trace metal bioaccumulation has been more

conclusive for Corbicula than for other freshwater mussel species. Abaychi and Mustafa (1988)

found temporal variability in the concentrations of Cd, Cu, Fe, Mn, Ni, Pb, V and Zn in water,

suspended particles and Corbicula tissue. However, they did not determine the statistical

significance of this fluctuation or the causal agent, limiting the strength of their conclusions. In a

3-year study in Suisun Bay, Luoma et al. (1990) showed seasonal fluctuations in Corbicula

condition (calculated dry weight of a 3.5 cm clam) and Cd, Cr, Cu and Zn in both Corbicula and

fine sediment. They found a correlation between Cd and Cu with the flow regime of the

Sacramento River but determined that temporal change in Cr accumulation was driven by

emissions from a local steel plant. They recommended temporally intensive monitoring, with

yearly averages being used to determine spatial trends in trace element contamination.

15

Relations Between Trace Element Accumulation and Bivalve Age and/or Size

Another oft-sited consideration in freshwater bivalve biomonitor studies is the size or age

of the organism (Millington 1983, Elder 1991). Studies using freshwater mussels that address

this factor are aptly summarized in Metcalfe-Smith et al. (1996). Few general conclusions can

be drawn about the relation between mussel size and metal accumulation. Metcalfe-Smith et al.

concluded that size and age should be included when designing a monitoring program but that

distinctions should be made between “contaminated” and “clean” sites, which will show

different ranges of accumulation. They also suggested that condition (tissue dry weight / shell

volume) serves as a better indicator of size than shell length for many elements (e.g. Cd, Cu, Mn

and Pb). However, Perceval et al. (2006) found that age was not a good indicator of metal

accumulation in P. grandis.

With regards to shell length as a variable affecting accumulation, broad conclusions are

also difficult to draw. Naimo et al. (1992) found Zn to be significantly concentrated and Cu to

be significantly less concentrated in larger individuals of Amblema plicata plicata. However, Cd

and Hg accumulation showed different relationships to growth depending on the sampling site.

A potential confounding factor in the results presented by this study was their use of size classes,

which essentially transformed length from a continuous variable into a discrete variable. They

also did not allow mussels to depurate before dissection, which meant that they measured metals

sorbed to gut contents as well as those assimilated into tissue. A similarly limited study found a

larger size class of Mytilaster lineatus to accumulate significantly higher concentrations of Cu

and lower concentrations of Pb than a smaller size class (Pourang 1996).

Comparing size classes to determine correlations between accumulation and length has

also been used in studies utilizing Corbicula as a biomonitor, probably to facilitate pooling of

16

tissues in for analysis. The size class approach was used by Abaychi and Mustafa (1988) to

conclude that smaller Corbicula accumulated significantly higher concentrations of Cu, Ni, Pb

and Zn as well as lower concentrations of Cd. In clams sampled from the Rio de lat Plata

(Argentina), Bilos et al. (1998) found larger size classes to accumulate higher concentrations of

Cu and lower concentrations of Zn. Sebesvari et al. (2005) showed a positive correlation

between size class and Sn accumulation but not As accumulation. In their study of a mining-

impacted watershed, Angelo et al. (2007) found lower concentrations of Cd in smaller size

classes but larger concentrations of Pb. Comparing size classes from a relatively uncontaminated

site to data from more contaminated sites, they concluded that environmental factors, rather than

individual clam size, accounted for more spatial variation.

The problem with assessing the effects of organism size using size classes is that

Corbicula have been shown to spawn in distinct, homogenously sized cohorts in certain systems

(Stites et al. 1995). In other words, effects on accumulation implied to be a function of growth

may be a function of cohort age. Because Corbicula grow at different rates under different

conditions, size classes cannot easily be applied across multiple sites where cohorts may grow at

different rates (i.e. this would involve comparing older, slower growing cohorts to younger,

faster growing cohorts). Using lengths and tissue concentrations of individuals, Peltier et al.

(2008) found a significant negative relationship between length and Cd accumulation but no

correlation with As, Cu, Hg, Se or Zn. Because length only explained 3-9% of variation in trace

element concentrations, they concluded that land cover was a better predictor. Clearly, more

work needs to be done in examining the relationship between size and the accumulation of trace

elements. Advanced analytical techniques can analyze individual organisms, allowing size to be

used as a continuous, rather than a discrete variable. Removing the variability related to size

17

from bioaccumulation data will ensure that erroneous conclusions are not drawn about the

relative contamination of different sites.

Relating Bioaccumulation to Water and Sediment Sources of Trace Elements

A central idea behind many biomonitoring efforts is that biomonitor organisms take up

trace elements from the environment and thus their levels of bioaccumulation can be correlated

to concentrations of that element in the local environment. This approach has been employed to

some extent with various freshwater mussels. Adams et al. (1981) found A. perplicata to

accumulate higher concentrations of Zn and Cd at sites with higher concentrations of those

elements in the sediments. D. polymorpha from 20 sites on the Mosel River showed no

correlation between tissue and water concentrations of Cd, Cr, Cu and Zn but did show a

correlation for Pb (Mersch 1992). Five sites in the Mirgenbach reservoir showed a tissue/water

correlation with Cu but with none of the other metals. Looking at several tissues and several

environmental compartments, Gundacker (2000) correlated tissue concentrations of Pb and Zn in

Anodonta to fine (< 23 μm) sediments, and Cd in U. pictorum tissues to concentrations in the

sediments. The general trend seen in data gathered from freshwater mussels is for stronger

correlations between bioaccumulated trace elements and the concentrations in local sediments,

rather than those in ambient waters at the time of sampling.

Many more biomonitoring studies of this type have been conducted using Corbicula,

with many of them reviewed by Doherty (1990). For example, laboratory studies have

demonstrated that Corbicula can accumulate Cd, Cu and Zn from water exposure in a dose-

dependent manner (Graney et al. 1983); accumulate Cu from water in a dose-dependent manner

(Harrison et al. 1984); accumulate Pb but not Cd from sediments (Tatem 1986); and accumulate

Cd and Hg from the water column in a dose-dependent manner (Inza et al. 1998). Most studies

18

carried out in aquaria and artificial streams tend to focus on the overlying water column as the

primary source of metals. Taking this approach is complicated by the natural response of

Corbicula to contaminants, which is to close valves and lower respiration in order to limit

exposure (Doherty et al. 1987). In other words, Corbicula will bioaccumulate trace elements in a

manner that is more likely to reflect chronic exposure to trace elements rather than brief

exposure. As a longer term depository of trace elements, sediments are more commonly

correlated to Corbicula tissues in field studies (Foster and Charlesworth 1996).

In a field study on the Apalachicola River, Elder and Mattraw (1984) found a significant

correlation between fine sediment and Corbicula tissue concentrations for As but not for Cd, Cr,

Cu, Pb or Zn. This finding contradicts several studies and is most likely due to Elder and

Mattraw’s difficulty in detecting elements in Corbicula tissues. They also collected Corbicula

and sediments from separate, nearby sites but treated them as though they came from the same

site. More recently, Villar et al. (1999) concluded that Cd accumulation in Corbicula and

Limnoperna fortunei was linked with sediment loads, while Cu and Zn accumulation was not

related to sediment concentrations. Andrès et al. (1999) found that Cd accumulated in relation to

the amount of Cd in the water column but that Zn showed no similar correlation. A number of

other studies previously discussed in this review have shown poor agreement between Corbicula

and water Cd, Cu, Fe, Mn, Ni, Pb, V and Zn (Abaychi and Mustafa 1988); apparent, but non-

significant, correlations between sediment and Corbicula Cd, Cr, Cu, Pb and Zn (Luoma et al.

1990); and stronger relationships between bioaccumulation and free ion concentrations

(calculated from sediment) for Cd than for Cu and Zn (Perceval et al. 2006). A repeated finding

in field surveys of Corbicula is that trace element concentrations in clam tissues are more likely

to be correlated to sediment concentrations than water. However, essential metals such as Cu

19

and Zn seem to be less likely to show a correlation between tissue and environmental

compartments than nonessential elements such as Cd. This is not surprising, considering the

higher biochemical controls that are exerted on Cu and Zn concentrations. Conclusions on the

environmental compartments that can be linked to bioaccumulation must therefore be made on

an element-by-element basis.

Relating Bioaccumulation to Adverse Effects

The ultimate goal for biomonitoring studies it to relate metrics that are analyzed (e.g.

tissue concentrations of a metal) with both organism impairment and human activity. In this

way, the amount of impairment caused by human activities can be quantified and monitored.

Attempts to connect mussel bioaccumulation of metals to deleterious effects in the environment

have met with mixed results. For instance, while Vinot & Pihan (2005) found that D.

polymorpha accumulated Cu from algae and transferred it to molluscivorous fish, they could

only speculate on potential ecotoxicological effects. Using P. grandis, Couillard et al. (1995a)

attempted to link bioaccumulation to all levels of organization. Their study examined

biomarkers (cellular level), growth (individual level) and reproductive (population level)

endpoints in P. grandis with increased Cd and Zn accumulation. They found increased oxidative

stress and decreased growth with metal accumulation but could not unambiguously make

conclusions at the population level because of the difference in response between transplanted

and local mussels (Couillard et al. 1995a). A later study using the same protocol found that the

calculated free ion of Cd2+ could be correlated to decreased population and fecundity endpoints

(Perceval et al. 2004). However, much of the variation in Cd2+ could be explained by

environmental factors such as dissolved organic matter and degree days (a function of time that

varies by temperature), thus indicating that lake morphometry could be structuring populations

20

more than Cd contamination. A 13-year study in the same smelter-impacted region of Canada

found that metal accumulation in P. grandis decreased as smelter emissions decreased (Perceval

et al. 2006) proving its effectiveness as a biomonitor of smelter pollution. Finally, in one of the

more convincing studies, Angelo et al. (2007) found that local extirpation or decreased richness

of native mussel assemblages was correlated to higher levels of Cd, Pb and Zn in mussel tissues

and fine sediments.

These studies demonstrate the difficulties in initiating and interpreting the results of a

biomonitoring program based on the bioaccumulation of trace elements by mussels. In addition

to the considerations already discussed, numerous papers have noted differences in accumulation

between different mussel species sampled at the same site (Van der Velde 1992, Metcalfe-Smith

et al. 1996, Gundacker 2000, Angelo et al. 2007). Because freshwater species can have fairly

restricted ranges, it can be difficult to compare the results of one biomonitor to another in

locations where different species overlap.

As with freshwater mussels, few studies have attempted to link trace metal

bioaccumulation to endpoints that demonstrate a detrimental effect to Corbicula. Returning to

Luoma et al. (1990), this study correlated decreased condition (calculated dry weight of a 3.5 cm

individual at a certain sampling site/date) with Cd and Cu in sediment and Corbicula tissue.

Paggi et al. (2006) examined the entire benthic community in a contaminant plume and found

Corbicula to be a sensitive species to Cr pollution but did not measure bioaccumulation. Other

studies have found Corbicula survival and growth to react to inputs of acid mine drainage and

nutrients (Soucek et al. 2001), as well as impacts from other coal-related activities such as

mining, burning, and waste disposal (Hull et al. 2006). In these studies, bioaccumulation of trace

metals was not measured, although contaminant sources were likely to contain increased loads of

21

trace elements. There is a large gap in our understanding between the bioaccumulation of trace

elements by biomonitor bivalves and the effects on the health of the aquatic system the

biomonitor organism represents.

Bioaccumulation of Trace Elements in the Shell Annuli of Bivalves

The calcium carbonate shells of freshwater bivalves have also been described as

potentially useful in the biomonitoring of trace element contamination (Imlay 1982). As they

grow, bivalves secrete sequential layer of shell to contain their increasing tissue mass. In many

climates, shell growth occurs in an annual fashion, with most growth occurring during periods of

the warmest water temperatures (Dunca et al. 2005). The patterns laid down by these layers are

typically used to age the bivalves, with sectioning the shell to reveal internal growth rings

considered the most accurate method (Neves and Moyer 1988). During shell growth, many trace

elements are incidentally incorporated into the shells of bivalves (Imlay 1982), making them

suitable as components of a biomonitoring study. More so than tissues, shells could theoretically

serve as long-term records of trace element exposure.

Early investigations into the trace metal accumulation in freshwater mussels are reviewed

thoroughly by Imlay (1982). These studies mainly used whole-shell analysis for metal

concentration, an approach that has not been wholly abandoned. Studies previously mentioned

in this study (Van der Velde 1992, Gundacker 2000) compared the trace element concentrations

in tissues of mussels collected in the field to concentrations in shells. In all instances, the shell

was found to accumulate (in some cases significantly) lower concentrations of trace elements.

Markich et al. (2002) found significant correlations between Velesunio angasi shell

concentrations of Co, Cu, Mn, Ni, U and Zn and the concentration found in the surface water (all

concentrations standardized by Ca). They further found significant correlation between Cu, Ni

22

and Zn concentrations in the shell to those in the sediment (sediment concentrations standardized

by Fe). Carroll and Romanek (2008) found that Mn and Sr in the shells of E. complanata from

various sites correlated to concentrations collected in the overlying water over a 2-year period.

Non-Toxic Trace Elements in Bivalve Shells: Ba, Mg, Mn and Sr

More detailed investigations of bivalve shells have approached them as recorders of

environmental condition and sampled within individual growth rings, attempting to determine

seasonal or annual differences in trace element concentration (see Table 2.2 for a summary of

studies). Earlier attempts at analyzing separate annuli relied on ashing shells and physically

breaking annuli apart (Imlay 1982, Neves and Moyer 1988). These approaches have been

superseded by newer instrumental techniques, which can analyze elemental profiles over a very

small area of shell. The principle technique used in this manner is laser ablation inductively

coupled plasma-mass spectroscopy (LA ICP-MS). The many analytical uses of LA ICP-MS are

reviewed in Russo et al. (2002), while the issues surrounding calibrating LA ICP-MS for studies

of biogenic calcium carbonate are explored in several papers (Vander Putten et al. 1999, Bellotto

and Miekeley 2000, Craig et al. 2000, Barats et al. 2007). These advances in analytical

capability have made it possible to analyze small fluctuations in the elemental composition of

shell annuli, potentially identifying seasonal trends or anomalous events resulting from increased

exposure to trace elements.

Strong evidence has emerged from studies on estuarine and marine bivalves that non-

toxic trace elements such as Ba, Mg, Mn and Sr are deposited into the shell in a manner driven

by environmental parameters. The estuarine clam Mya arenaria showed clear correlation

between peaks in Sr/Ca ratios and winter (low temperature) annuli (Palacios et al. 1994). The

same relationship was found in Spisula solidissima, while Mercenaria mercenaria shells showed

23

the opposite relationship to season (Stecher et al. 1996). Regular Sr peaks corresponding to

seasonal cycles of growth, have also been found in Arctica islandica (Toland et al. 2000), M.

edulis (Vander Putten et al. 2000), mangrove Isognomon ephippium (Lazareth et al. 2003),

Protothaca staminea (Takesue and Van Geen 2004) and in the scallop Pectens maximus (Freitas

et al. 2006). However, the cause of this annual fluctuation is a matter of debate. While Stecher

et al. (1996) demonstrated a correlation between Sr and δ18O [a standard measure of water

temperature at the time of shell formation; (Andrus and Crowe 2000)], Takesue and van Geen

(2004) argued that Sr peaks are more closely related to shell growth rate because their data could

not be well correlated to recorded water temperatures. Likewise, Vander Putten et al. (2000)

found that Sr did not co-vary with temperature. Modeling a larger number of environmental

parameters, Freitas et al. (2006) found temperature to best explain Sr/Ca shell formed in the

spring, while Mg/Ca best explained Sr/Ca incorporated in the summer, possibly due to the lattice

structure of calcium carbonate. These distinctions, while they do not change the seasonal

patterns of peaks, do indicate that Sr/Ca concentrations are the result of complex biogeochemical

processes.

Another environmental indicator analyzed in estuarine shells is Ba concentration. Ba

peaks in shell annuli are believed to represent times of greatly increased productivity, such as

periods of algal blooms (Stecher et al. 1996). In the same study discussed above, Stecher et al.

(1996) noted “sharp, episodic maxima” in Ba/Ca profiles that they associated with “episodic

phytoplankton blooms associated with increased nutrients and light fluxes in the spring.” Toland

et al. (2000) and Vander Putten et al. (2000) reported findings that agreed with this hypothesis,

while Lazareth et al. (2003) similarly found Ba/Ca peaks in a tropical coastal system were likely

driven by freshwater/nutrient inputs associated with the monsoon regime. A more thorough

24

approach by Gillikin et al. (2006) involving both laboratory studies and field transplants of M.

edulis firmly linked [Ba/Ca]shell to [Ba/Ca]Water. Although they could not link episodic Ba

maxima to chlorophyll a maxima at all sites, they concluded that barite formation during algal

blooms is one of the most likely answers for this phenomenon. Overall, the literature appears to

agree that Ba profiles in bivalve shells are governed by primary productivity and nutrient

availability.

A less well-researched element in bivalve annuli is Mg. Toland et al. (2000) assumed a

relationship between temperature and Mg defined the seasonal trends they found in the shells of

A. islandica. Year round Mg/Ca relationships to temperature were demonstrated by Lazareth et

al. (2003) and Takesue and van Geen (2004). However, Vander Putten (2000) found a strong

relationship between Mg/Ca in the shells of transplanted M. edulis and water temperature in the

spring that “broke down” during the summer months. Likewise, while Freitas et al. (2006)

observed a weak but significant correlation between temperature and Mg/Ca, they found that the

relationship differed during winter and summer months. The available findings therefore seem

to indicate that different controls affect Mg/Ca concentrations in marine or estuarine bivalves,

with temperature being a major factor for at least part of the year.

A final element thought to indicate environmental conditions is Mn. The first paper to

correlate Mn to estuarine environmental parameters is Vander Putten et al. (2000), who found

Mn to co-vary with Ba and chlorophyll a, thus linking Mn/Ca to primary productivity. The

findings of this study were seconded by that of Lazareth et al. (2003). However, Freitas et al.

(2006) determined that the seasonal Mn peaks seen in their study did not match local chlorophyll

a peaks or periods of Mn oxidation, but were more likely related to dissolved and particulate

concentrations of Mn. Unfortunately, this assessment was not based on directly measured Mn

25

concentrations during the study period but rather on records of seasonal Mn concentrations in the

study area from 20 years previous. Therefore, it may be more fitting to look at their findings in a

similar light to Ba analyses, in which Ba/Ca peaks could not be well correlated to the exact dates

of chlorophyll a peaks (Vander Putten et al. 2000, Gillikin et al. 2006).

Similar studies in freshwater bivalves have only been pursued in a few limited instances.

A study using Anodonta demonstrated possible seasonal fluctuations in Mn concentrations

(Lindh et al. 1988). The authors speculated that spring snow melt mobilized Mn, which was then

incorporated into the shell. A study employing freshwater pearl mussels, Margaritifera

margaritifera, found a poor relation of Sr/Ca to temperature but still found higher Sr

concentrations in winter growth bands and disturbance lines (Nystrom et al. 1996). Further, they

observed Mn peaks during times of high temperature and low dissolved oxygen, although they

did not attempt to correlate the two directly. Profiles of Mn obtained from Hyridella depressa

also showed seasonal peaks in Mn associated with organic-rich bands of winter growth (Siegele

et al. 2001). Langlet et al. (2007) found seasonal peaks of Mn in the shells of Pleiodon spekii

from Lake Tanganyika that they related to seasonal Mn upwellings and increased primary

productivity linked to the monsoon cycle. In their study of E. complanata, Carroll and Romanek

(2008) found that shells downstream of a lake showed regular peaks in Ba and Mn that they

speculated could correspond to phytoplankton availability. Furthermore, they found that a site

near a coal-fired power plant provided very different Ba, Mn and Sr profiles than those found at

other sites, which may indicate that pollution can alter the manner in which these trace elements

are incorporated into shells.

Explanations of trace element profiles in bivalve shells have in some cases agreed with

the conclusions of marine and estuarine researchers, for instance the finding that Mn is primarily

26

influenced by phytoplankton availability (Langlet et al. 2007, Carroll and Romanek 2008).

However, Nystrom et al. (1996) speculated that Sr was incorporated during growth cessation to

create higher density crystal lattices for protective purposes. They also saw a link in seasonal

lows of DO with peaks of Mn, caused by redox chemistry that made Mn more bioavailable.

With all of the many biogeochemical processes that go into bioavailability of trace elements and

their incorporation into bivalve shells, it is possible that freshwater bivalves act in a different

manner than their marine relations.

Potentially Toxic Trace Elements in Bivalve Shells

Trace elements linked to industrial contamination have not been studied as extensively as

Ba, Mg, Mn and Sr. Using only the most recent annuli of M. arenaria, Pitts and Walker (1994)

found direct correlation between [Pb]shell and [Pb]Water collected over a 2-year period. An early

use of LA ICP-MS by Raith et al. (1996) used a single A. islandica shell to identify Pb peaks that

may have correlated to increased loads entering Cardigan Bay (UK). Four individuals of

Cerastoderma edule were collected from 4 sites around the UK in Price and Pearce (1997). The

authors characterized As, Cu, Pb, U and Zn and found that metal profiles rarely matched one

another in the same shell and that metal concentrations often increased after winter growth

bands. Their complete lack of statistical analysis renders any relationship merely speculative.

Analysis of Crassotrea virginica shells by Huanxin et al. (2000) revealed variations in Cr, Fe,

Mn, Pb and Zn but the authors did not attempt to described the reasons behind the variation. In a

more concerted attempt to characterize trace element pollution using bivalve shells, Richardson

et al. (2001) compared three Modiolus modiolus from a North Sea (UK) site of known industrial

dumping to three shells from a non-dumping sites. Cu, Pb and Zn concentrations in the shells

were significantly higher at the dumping site. The metal profiles showed good agreement

27

between the shells at the same site and may have reflected a temporal decrease in trace metal

exposure following a ban on dumping industrial waste at sea. A similar approach was taken by

Liehr et al. (2005), using A. islandica from the Mecklenburg Bight (Baltic Sea;

Germany/Denmark). They also found higher Cu, Pb and Zn concentrations at the dumping site

but saw no pattern.

In a study limited by its minimal sampling, Chiffoleau et al. (2004) found V to have been

significantly enriched in a single specimen of P. maximus in the annulus laid down directly

following an oil spill. The laser ablation study was strengthened by biomonitor data from

mussels and oysters that showed increased tissue concentrations of Ni and V following the spill

but weakened by the use of only a single sample shell. Nevertheless, this is one of the few

studies that links trace element enrichment in shell annuli directly to a pollution event. In an

attempt to determine impacts of accelerated glacier loss and increased human activity in the

Antarctic, Dick et al. (2007) examined shells of Laternula elliptica. No increases in Al, Cu, Fe,

Mn or Pb were found in more recent annuli, although resolution was limited by the narrowness

of the growth rings being analyzed.

Lead has been more intensely analyzed than other potentially toxic trace elements. In an

attempt to validate incorporation of Pb into growing shell, MacFarlane et al. (2006) determined

that the pearl mussel Pinctada imbricata incorporated increased Pb concentrations into shell

during both sustained and chronic aqueous exposures. Vander Putten et al. (2000) found

underlying seasonal trends (broad maxima in spring and early summer) in Pb/Ca concentrations

in M. edulis but were unable to correlate them to seasonal fluctuations in Pb concentration in

water. Mercenaria mercenaria shells, both modern and archival, collected from Pamlico Sound

(NC, USA) were used to develop a long-term chronology by Gillikin et al. (2005). They found

28

significantly higher Pb/Ca in annuli coinciding with dates directly before the ban on tetra-ethyl

lead in gasoline but found no other intra- or inter-annual trends. Unfortunately, their “annual”

data was a mean of averaged intra-annual data that was then averaged again between shells from

sites that had different environments and significantly different Pb/Ca concentrations. A visual

inspection of their intra-annual data for four shells indicates apparent seasonal trends, similar to

those previously described in Vander Putten et al. (2000). The overall implication is that their

conclusion that bivalve shells do not constitute suitable archives of environmental Pb may be

mistaken.

Again, the literature is depaupurate when it comes to freshwater studies of the sort

discussed above, with a few studies using a wide variety of instrumental approaches. An early

study by Carrell et al. (1987) using a proton microprobe (μ-PIXE) found newer M. margaratifera

shells showed significantly lower levels of Ag, Au, Co and Fe than 19th century samples taken

from annuli laid down when mining in the region was active. Subsequently, a similar study

showed possible seasonal fluctuations in Al, Cu, Fe and Zn in Anodonta (Lindh et al. 1988).

Fossil D. polymorpha shells sampled in lake sediment cores were analyzed with LA ICP-MS for

Cd, Cu, Pb and Zn by Schettler and Pearce (1996). This study showed that older shells located

in less contaminated sediments accumulated lower levels of Cd, Cu, Pb and Zn, but annuli were

not sampled individually. Al-Aasm et al. (1998) digested whole annuli of D. polymorpha from

Lake Erie and determined that Cd, Cu, Fe, Mg, Mn and V increased during periods of warmer

temperatures and were higher at sites closer to sources of industrial contaminants, although they

supplied no statistical analysis to support this. A study utilizing micro-XRF spectrometry on a

modern (1992) and archival (1917) specimen of Unio crassus retzius was undertaken by

Kurunczi et al. (2001). This approach allowed only a qualitative comparison, with the newer

29

shell surface showing As, Cu and Pb not present in the archived sample. Metal profiles were

attributed to the dubious provenance of sediments preserved on the shell surface, limiting their

usefulness. Markich et al. (2002) found significantly higher concentrations of Co, Cu, Mn, U

and Zn in V. angasi from a site impacted by mine waste when compared to reference sites, with

shell concentrations linearly correlated to water concentrations. They also found that Cu and Zn

in annual layers declined after the mine-waste site was remediated. Carroll (2008) used LA ICP-

MS to analyze Cu in E. complanata and found increased concentrations at organic-rich winter

growth bands, implying that Cu was associated with the organic fraction of the shell, which tends

to increase during winter growth.

Summary

That environmental factors exert strong controls on incorporation of Ba, Mg, Mn and Sr

into bivalve shells is supported by numerous studies. The challenges for the future in this field

will be two-fold. First, the individual agents of environmental control need to be investigated

further, so that trace element profiles can be used as reliable chronographs of environmental

conditions at a given time in an organism’s life. Secondly, the same approaches that have

successfully identified potential environmental controls in estuarine and marine systems should

be applied more extensively to freshwater systems, especially lotic systems. Applying this

approach to lotic systems may prove more challenging because the more variable environment of

freshwater, especially lotic, systems may lead to more variable data.

Summarizing the current findings for metals such as Cu, Pb and Zn is more difficult than

those for Ba, Mg, Mn and Sr. That Cu, Pb and Zn can become enriched in the shells of bivalves

with increased exposure is supported by several studies but the utility of retroactively monitoring

shell annuli is not always clear. Much work needs to be done in order to use bivalve shell annuli

30

as archives of trace element pollution, especially in freshwater systems. Firstly, seasonal

environmental parameters controlling contaminant uptake need to be taken into account to

eliminate naturally occurring peaks in concentrations. Secondly, the regularity with which

bivalves record the impacts of short-term pollution events needs to be validated. Validation

could be performed through laboratory and mesocosm studies or by surveying populations that

have recently seen increased exposure. By validating these approaches, bivalve shells could be

transformed into long-term, retroactive records of trace element pollution that would require

much less time and effort to sample than traditional biomonitoring approaches.

Conclusions

A large body of work has grown up around the idea of using freshwater bivalves in

general and Corbicula fluminea, in particular, as biomonitors of potentially toxic trace elements.

Although it has been well established that bivalves exposed to higher levels of trace elements

will accumulate higher concentrations in their tissues, the many factors that affect

bioaccumulation are not fully understood. Two leading models of trace element

bioaccumulation (biodynamic and biogeochemical) indicate that element sources in particulates

and sediments contribute the most to accumulation. Together, these studies call into question the

importance of bioaccumulation and toxicity studies using water as a primary source. Likewise,

individual size and season of collection have been implicated as affecting bioaccumulation but

the specific effects of these factors remain limited to the species, element, site and methodology

of a particular study. Meanwhile, numerous field studies have indicated that anthropogenic

inputs (as measured in particulates and sediments especially) of trace elements better determine

bioaccumulation than other confounding affects. Continued investigation into the individual and

environmental factors that influence trace element accumulation in bivalves, as well as studies

31

that link bioaccumulation directly to adverse effects in wild populations, will greatly increase

their usefulness as biomonitors. Meanwhile, a promising aspect of biomonitoring is analyzing

the accumulation of trace elements in bivalve shells. Understanding the environmental controls

of this process and validating the ability of bivalve shells to record fluctuations in metal

concentrations may eventually enable retroactive monitoring of trace element pollution.

32

Table 2.1. A table summarizing the criteria of good biomonitors, how closely they are met by freshwater bivalves, with references relating to the appropriate point.

Criterion Suitability of Bivalves References 1. Tolerance of contaminants

Typically resilient to contaminants; can be dependent on species and contaminant.

(Elder 1991, Naimo 1995)

2. Limited mobility Mostly sedentary; move very little even during storm surges

(Imlay 1982, Schwalb and Pusch 2007)

3. Long life span Unionidae: up to 100 years; Corbiculidae: 1-4 years

(McMahon 2001, Vaughn and Hakenkamp 2001)

4. Ubiquity and abundance

Typically colonize in large, dense “beds”; distribution can be patchy

(Strayer 1999, Morales et al. 2006, Strayer 2008)

5. Tissue amount Wide range of sizes; smaller species may have to be analyzed in composite samples

(Elder 1991, McMahon 2001, Vaughn and Hakenkamp 2001)

6. Ability to survive disturbance

Can be dependent on species; by sealing its valves, a bivalve can survive most

short-term disturbances

(Elder 1991, Vaughn and Hakenkamp 2001)

7. Easily identified and sampled

Sampling can occur in wadeable portions of streams; identification of certain

species may be problematic

(Campbell et al. 2005, Meador 2008)

8. Displays a metric of exposure

Metrics can include: Biomarkers, bioaccumulation, growth, fecundity, acute

toxicity, etc.

Numerous: See this chapter

9. The metric correlates to environmental health

May be problematic; see this chapter for more detailed analysis.

Numerous: See this chapter

33

Table 2.2. Field studies that have analyzed intra- and/or inter-annual variability in trace element concentrations of bivalve shells. Environment Species Instrumentation Elements Citation

Marine Arctica islandica LA ICP-MS Sr, Pb Raith et al. 1996 Spisula solidissima LA ICP-MS Ba, Sr Stecher et al. 1996 Cerastoderma edule LA ICP-MS As, Cu, Pb, U, Zn Price and Pearce 1997 Crassotrea virgina GF-AAS Cd, Cu, Cr, Fe, Mn, Pb, Zn Huanxi et al. 2000 Arctica islandica LA ICP-MS Ba, Mg, Sr Toland et al. 2000 Modiolus modiolus LA ICP-MS Cu, Pb, Zn Richardson et al. 2001 Pecten maximus LA ICP-MS Ni, V Chiffoleau et al. 2004 Protothaca staminea LA ICP-MS Mg, Sr Takesue and van Geen 2004 Mercenaria mercenaria LA ICP-MS Pb Gillikin et al. 2005 Arctica islandica LA ICP-MS Cu, Pb, Zn Liehr et al. 2005 Pecten maximus LA ICP-MS Mg, Mn, Sr Freitas et al. 2006 Laternula elliptica LA ICP-MS Al, Cu, Fe, Mn, Pb and U Dick et al. 2007

Estuarine Mya arenaria LA ICP-MS Sr Palacios et al. 1994 Mercenaria mercenaria LA ICP-MS Ba, Sr Stecher et al. 1996 Mytilus edulis LA ICP-MS Ba, Mg, Mn, Pb, Sr Vander Putten et al. 2000 Isognomon ephippium LA ICP-MS Ba, Mg, Mn, Sr Lazareth et al. 2003 Mytilus edulis LA ICP-MS Ba Gillikin et al. 2006

Lentic Anodonta μ-PIXE Al, Cu, Fe, Mg, Mn, Ni, Sr, Zn Lindh et al. 1988 Freshwater Dreissena polymorpha LA ICP-MS Cd, Cu, Pb, Zn Schettler and Pearce 1996

Dreissena polymorpha ICP-MS Cd, Cu, Fe, Mg, Mn, Pb, V Al-Aasm et al. 1998 Pleiodon spekii LA ICP-MS Mn Langlet et al. 2007

Lotic Margaratifera margaratifera μ-PIXE Al, Cu, Fe, Mg, Mn, Ni, Zn Carrell et al. 1987 Freshwater Margaratifera margaratifera SLIM-UP Mn, Sr Nystrom et al. 1996

Unio crassus retzius μ-XRF Cu, Fe, Mn, Pb, Sr, Zn Kurunczi et al. 2001 Hyridella depressa μ-PIXE Mn Siegele et al. 2001 Velesunio angasi SIMS Cu, Co, Fe, Mn, Ni, Pb, U, Zn Markich et al. 2002

34

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Tatem, H. E. 1986. Bioaccumulation of polychlorinated biphenyls and metals from contaminated sediment by freshwater prawns, Macrobrachium rosenbergii and Clams, Corbicula fluminea. Archives of Environmental Contamination and Toxicology 15:171-183.

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45

CHAPTER THREE

BIOACCUMULATION OF TRACE ELEMENTS BY CORBICULA FLUMINEA AND

ELLIPTIO HOPETONENSIS IN THE ALTAMAHA RIVER SYSTEM1

Abstract

The Asian clam, Corbicula fluminea, has frequently been used as a biomonitor of a wide

array of pollutants, including potentially toxic trace elements. This study analyzed the tissue

concentrations of As, Cd, Cr, Cu, Hg, Mn, Ni, Pb and Zn in Corbicula and the native mussel

Elliptio hopetonensis from 14 sites in the Altamaha River system, in Georgia, USA.

Concentrations (μg/g) of As, Cd, Cu, Hg and Pb were found to be significantly positively

correlated (α = 0.05) between the two species, indicating that Corbicula bioaccumulation can be

used as a proxy for co-occurring E. hopetonensis. When calculated as body burdens (g), As, Cd,

Cu, Hg and Zn were significantly positively correlated (α =0.05) between the two species. The

trace element tissue concentrations and body burdens in both Corbicula and E. hopetonensis

were correlated to a number of environmental and growth parameters. Lower water temperature,

dissolved oxygen, alkalinity, and hardness tended to lead to higher concentrations of trace

elements, while larger organisms tended to accumulate higher concentrations. Only Cd, Cu, Ni

and Pb showed any correlation between tissue concentrations of either species and

concentrations in water and/or sediment. No obvious longitudinal trends could be seen from the

data. This study supports the idea that bioaccumulation of trace elements by Corbicula can be

1 Shoults-Wilson WA, Unrine JM, Rickard J, Black MC. To be submitted to Science of the Total Environment

46

used to approximate levels accumulated by co-occurring native mussel species as part of a

biomonitoring program.

Introduction

Native mussel species have been declining across the southeast (Williams et al. 1993).

The Altamaha River in southeastern Georgia contains a native assemblage of 19 mussels. The

federally endangered species Elliptio spinosa is of special concern (Meador 2008). Threats to

native mussels include sedimentation and habitat alteration, loss of host fish species, exposure to

anthropogenic contaminants and competition from invasive species (Williams et al. 1993, Naimo

1995). As an invasive species, Corbicula has been implicated as a factor in native mussel

decline through competition for food resources (Sickel 1973, Kraemer 1979, Elder 1991).

A widespread invasive freshwater bivalve, the Asian clam (Corbicula fluminea; hereafter

Corbicula) was first found along the Pacific coast of the United States in 1937 (Kraemer 1979).

Since then it has spread to 33 of the 49 continental states, its range limited by colder winter

temperatures (Stites et al. 1995). Spreading to the southeastern states, Corbicula became

established in all Georgia watersheds, including the largest Atlantic Slope drainage, the

Altamaha River (Sickel 1973, Stites et al. 1995). It has since established itself ubiquitously and

abundantly throughout the Altamaha and its tributaries.

Corbicula has been studied extensively as a biomonitor, or an organism whose

accumulation of or reaction to contaminants is used as a measure of the impacts of those

contaminants on the environment. It has become popular as a biomonitor for both potentially

toxic trace elements (Caldwell and Buhler 1983, Graney et al. 1983, Elder and Mattraw 1984,

Graney et al. 1984, Tatem 1986, Abaychi and Mustafa 1988, Doherty et al. 1990, Luoma et al.

1990, Pourang 1996, Baudrimont et al. 1997, Andres et al. 1999, Villar et al. 1999, Angelo et al.

47

2007) and organic compounds (Elder and Mattraw 1984, Tatem 1986, Doherty 1990). The

general consensus found in the literature is that Corbicula can serve as a good biomonitor

organism for a variety of contaminants due to its ubiquity, high abundance, low mobility, rapid

accumulation of contaminants and tolerance to high amounts of contamination (Doherty 1990).

Of primary interest in past studies has been the bioaccumulation of contaminants by Corbicula

following exposure (Graney et al. 1983, Doherty 1990). Elevated bioaccumulation of trace

elements by freshwater bivalves has been linked to adverse effects such as oxidative stress,

reduced fecundity and local extinctions of native mussels (Couillard et al. 1995, Perceval et al.

2004, Angelo et al. 2007). Few studies have made direct comparison between the accumulation

of contaminants by Corbicula and that of co-occurring native mussel species (Caldwell and

Buhler 1983, Angelo et al. 2007).

In order to better evaluate the utility of Corbicula as a biomonitor organism, this study

examined 1) the difference (if any) in the amount of trace metals accumulated by co-occurring

Corbicula and the native Unionid mussel species Elliptio hopetonensis at multiple sites within

the Altamaha River system; 2) the ability to draw significant correlations between Corbicula

accumulation and that of a native species; 3) spatial and environmental factors that may

influence bioaccumulation. By elucidating the link between Corbicula and E. hopetonensis in

their bioaccumulation of trace metals, the groundwork could be laid for trace metal

biomonitoring programs using Corbicula exclusively. Such a program would allow the

assessment of the threat of trace metals to native mussels without disrupting their communities

with destructive sampling.

48

Materials and Methods

Study Area

The Altamaha River drainage is completely contained within the state of Georgia. The

Altamaha River proper is formed by the confluence of the Ocmulgee and Oconee Rivers, whose

headwaters are in the piedmont phyto-geographic province and cross the fall line at Macon and

Milledgeville GA, respectively (Reese and Batzer 2007). The Altamaha proper has a largely

unaltered channel, bordered by wide areas of bottomland forest, while the Oconee and Ocmulgee

both have reservoirs in their middle or upper reaches. Other major tributaries are the Little

Ocmulgee and Ohoopee Rivers. While many of its smaller tributaries below the fall line could

be considered to be blackwater systems (Patrick 1994), the Altamaha mainstem is not a classic

example of a blackwater river (Stites et al. 1995).

Fourteen sites were chosen for this study, two on the lower Oconee (OCN), four on the

middle and lower Ocmulgee (OMG), one on the lower Ohoopee (OHP), and seven on the main

channel of the Altamaha (ALT; Figure 3.1). These sites were chosen based on their location

within the Altamaha system (for broad representation), their downstream proximity to potential

sources of trace metals (population centers and industry) and the co-occurrence of Corbicula and

E. hopetonensis. Habitats sampled ranged from depositional sloughs separated from the river by

a small sandbar, to the leading edges of sandbars, to rocky shelves of the unique “Altamaha grit”

microhabitat. GIS coordinates were taken and recorded for each site at the time of sampling

(Magellan explorist 210; Thales Navigation, San Diego CA), and a sketch of the site was made

indicating major features of substrate and local morphology.

49

Water Quality and Stream Parameters

At each site, the following water quality parameters were measured immediately

upstream of the sampling site: temperature and dissolved oxygen (YSI Model 55; Yellow

Springs OH, USA), conductivity (YSI Model 85), and pH (Orion Model 720A; Beverly MA,

USA). Nalgene bottles previously washed in 10% trace metal grade HNO3 were used to collect

500 mL of water at each site. The water was kept cool and refrigerated upon return to the lab.

Subsamples (50 mL) were used to determine alkalinity and hardness titrametrically (Hach®,

Loveland CO, USA).

Sediment and Bivalve Collection

Sediment was collected at each site using a petite Ponar grab sampler until 3 grabs had

been made or >500 mL of sediment had been acquired. Composite sediments were homogenized

in an acid-rinsed plastic container and 500 mL was subsampled into a 10% trace metal grade

HNO3 washed nalgene bottle. The sediment was stored on ice for transport back to the lab and

stored at 4°C. Further processing took place within 2 weeks of collection.

At each site, Corbicula (25-35) and E. hopetonensis (5) were sampled by hand and

deposited in labeled mesh bags. They were stored in a bucket of aerated river water, which was

placed in a cooler with small amounts of ice to limit stress to the organisms. Upon transportation

to the lab, they were transferred to a climate controlled room and allowed to depurate gut

contents for ~12-36 h.

Sample Processing

Water samples were filtered through a 0.5 μm paper filter that had previously been rinsed

with 10% trace metal grade HNO3 and dried. Approximately 50 mL of each filtered sample

were poured into an acid-washed nalgene bottle and acidified with 0.5 mL of trace metal grade

50

HNO3 in order to maintain the integrity of trace metal concentrations. Samples were stored at

room temperature until analysis.

All sediment samples were processed using a protocol adapted from Luoma et al. (1990).

Sediment samples were re-homogenized and poured through a 1 mm acid-washed plastic sieve.

The resulting < 1 mm fraction was placed in acid-washed nalgene containers and stored at –20°C

until they could be processed further. Frozen samples were thawed under refrigeration at a later

date and wet-sieved again through a 105 μm Nytex mesh (Spectrum Mesh; Rancho Dominguez

CA, USA) into an acid-washed 600 mL beaker. Fine fractions were dried at temperatures

ranging from room temperature to 95°C, homogenized using a dry acid-washed mortar and pestle

and stored in acid washed plastic centrifuge tubes under anhydrous conditions until digestion.

Bivalves were measured for maximum length, height, and depth of shells while still alive.

Whole tissues (8-10 Corbicula and 5 E. hopetonensis) were dissected from their shells and

placed on pre-weighed aluminum pans. Wet weights of tissues were measured and the tissues

were then dried at 100 °C for 48 h or until completely dry, at which point they were reweighed.

Because of its much larger tissue mass, E. hopetonensis tissues were homogenized using an acid-

washed mortar and pestle. Corbicula samples were stored as whole tissues. All dried tissues

were stored in acid-washed plastic containers until digestion. Shells were allowed to dry, then

weighed, labeled in pencil and stored in plastic bags. Left valves of shells were filled with sand

of a known density and weighed in order to estimate shell volume (Kesler 2004).

Acid-Digestion

Between 0.25 and 0.5 g of dried fine fraction sediment were completely digested in 8 mL

trace metal grade HNO3 and 3 mL HF, using a MARS 5 HP 500 Plus system (CEM Corp;

Matthews NC, USA), under the conditions of EPA protocol 3052 (USEPA 1996). Three

51

replicate samples were digested for each sediment composite. All digestions were diluted in 24

mL of Milli-Q water to ~33% acid. Blanks and standard sediments (MESS-3, National Research

Council of Canada, Ottawa ON, Canada; NIST 1645, National Bureau of Standards,

Gaithersburg MD, USA) were also digested with each run. All samples, blanks and standards

were stored at room temperature in acid-washed centrifuge tubes until analysis.

Whole tissues of Corbicula and 0.10-0.25 g homogenized tissues of E. hopetonensis were

completely digested in 5 mL of trace metal grade HNO3, using a MARS 5 HP 500 Plus system

(CEM Corp; Matthews NC, USA), under the conditions of EPA protocol 3015 (USEPA 1994).

Three replicate digestions were performed for each individual E. hopetonensis. Digestions were

diluted with 10 mL Milli-Q water to ~33% acid. Exact concentrations were calculated using dry

tissue mass and the mass of the final solution. One blank (acid only) and one standard reference

material (SRM) of ~0.075 g TORT-2 lobster hepatopancreas (National Research Council of

Canada, Ottawa ON, Canada) were digested in each run. Dilute samples, blanks and standard

were stored at room temperature until analysis.

Sample Analysis

Analyses of digested samples were carried out as described in Unrine et al. (2007). Trace

element concentrations in digests (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb and Zn) were quantified using

a Perkin-Elmer Sciex Elan DRC Plus inductively coupled plasma-mass spectrometer (ICP-MS;

Norwalk CT, USA) operating in standard mode. Method detection limits (MDLs) were

calculated using blank digests. Mean MDLs ranged from 0.0250 μg/g for Cd to 3.360 μg/g for

Cu. Recoveries of SRMs were in good agreement with certified values and averages ranged

between 85.27% for As and 130.04% for Pb. However Cr recovery was highly variable because

of carbon interference and Cr concentrations from those samples were not used in data analysis.

52

Relative percent difference (RPD) of replicate dilutions of sub-samples was used to approximate

analytical error. On average (n=34) RPD ranged from 2.01% for As and Zn to 12.80% for Pb.

Spikes of ~2-5 times the un-spiked sample concentration had mean recoveries ranging from

91.61% for Cr and 101.87% for Ni (n=33).

Statistical Analysis

Concentrations of metals were calculated on a μg/g dry tissue weight basis for both

Corbicula and E. hopetonensis and were compared at each site. Analysis of variance (ANOVA)

was used to determine significant differences in metal concentrations between species followed

by Duncan’s post hoc test (α = 0.05). Calculations were performed using Statistical Analysis

Software (SAS v9.1, SAS Institute, Cary NC, USA). Similarly, ANOVA was used to determine

a significant difference in accumulation of an element between a site and the closest upstream

site. Tissue concentrations of each element were averaged for a given species at each sites. The

mean concentrations of an element for each species were correlated across sites using linear

regression and an F-test performed to determine the significance (α = 0.05) of the correlation.

Finally, tissue element concentrations at each site were compared to environmental

concentrations of the element (water and sediment) at each site, as well as the water quality

parameters (temperature, pH, etc) at each site using linear regression. F-Tests were used to

determine any significant (α = 0.05) correlations between these outside factors and accumulation

of trace metals in the two species.

Results

The results of the comparisons of element accumulation between Corbicula and E.

hopetonensis are summarized in Table 3.1 (mean concentrations of elements, organized by site

and species, are included in Appendix 2.1). Overall, Corbicula tended to accumulate

53

significantly higher concentrations of As, Cd, Cu, and Hg. E. hopetonensis tended to accumulate

significantly higher concentrations of the elements Mn. Samples that were below detection

limits reduced the number of sites at which a comparison could be made for Ni and Pb (to 11 and

5 respectively). Highly variable Cr recovery from SRMs decreased the number of sites with

usable data (to 7). These elements, as well as Zn, did not reveal trends of accumulation that were

clear, although Corbicula tended to accumulate higher concentrations of Cr and Ni, while E.

hopetonensis tended to accumulate higher levels of Zn. It should be noted that at sites where Cr,

Ni, and Pb were below detection in Corbicula, they were readily detectable in E. hopetonensis.

The log transformed concentrations of the elements As, Cd, Cu, Hg and Pb found in

Corbicula were significantly (α = 0.05) correlated to those found in E. hopetonensis (Figure 3.2).

The log transformed body burdens of the elements As, Cd, Cu, Hg and Zn found in Corbicula

were significantly (p < 0.05) correlated to those found in E. hopetonensis (Figure 3.3). All

correlations were positive, with higher concentrations and body burdens of an element in

Corbicula corresponding to higher concentrations and body burdens in E. hopetonensis.

Upstream/downstream comparisons for selected elements (As, Cd, Cu, Hg, Pb, and Zn)

are represented in Figure 3.4. No clear longitudinal trends can be drawn from these data.

Notably, the site on the Ohoopee River (OHP1) exhibited significantly elevated levels of Cd, Cu,

Hg, Pb, and Zn. The two species also tended to show increased or decreased element

concentrations in tandem with one another. This further supports the use of Corbicula as a

surrogate of E. hopetonensis.

There were numerous correlations between environmental parameters and the mean

concentration of accumulated elements. The significant (α = 0.05) correlations are listed in

Table 3.2. Water quality parameters (pH, temperature, dissolved oxygen, alkalinity and

54

hardness) were negatively correlated, while size metrics [shell length, height, depth, thickness

index and condition index: dry tissue mass / shell volume (Kesler 2004)] and concentrations of

the elements in water and sediment typically formed positively correlated. Body burdens of both

species had a greater tendency to correlate with size metrics and were also more likely to be

correlated with certain water quality parameters, such as temperature and dissolved oxygen. On

the other hand, tissue concentration was more likely to be correlated with water alkalinity and

hardness. With respect to the concentrations or burdens of an element reflecting the amounts

found in the environment, only Cd, Cu, Ni, and Pb showed any correlation between amount

accumulated and the amount found in water and/or sediment samples. Overall, Corbicula tended

to correlate with fewer parameters, whether growth or environmental, than E. hopetonensis.

Discussion

Comparison of trace element bioaccumulation between Asian clams and native mussels

It has been suggested that the Asian clam Corbicula is a suitable biological monitor of

potentially toxic trace metals (Doherty 1990). This assertion comes primarily from comparisons

between Corbicula and environmental concentrations of metals in compartments such as water

and sediment. The typical approach has been to validate the ability of tissue concentrations in

Corbicula to indicate increased environmental concentrations. This study has attempted to

validate Corbicula’s suitability as a biomonitor by comparing it to another species, in particular

that of a native Unionid mussel collected in the field.

Angelo et al. (2007) investigated the accumulation of Cd, Pb, and Zn by Corbicula as

well as various co-occurring native mussel species (14 species not differentiated in their data

analysis). They found that Corbicula tissue concentrations significantly correlated to mussel

concentrations. Similarly, in this study, accumulation of higher concentrations of As, Cd, Cu,

55

Hg, and Pb by Corbicula were correlated with higher concentrations in E. hopetonensis (Figure

3.2). This supports the idea that metal concentrations accumulated by Corbicula can be used to

estimate those accumulated by co-occurring populations of native mussels. It is likely that

biomonitoring programs that solely sample Corbicula can provide an accurate representation of

the exposure of native Unionids to trace element pollution.

While mean concentrations are well correlated when compared between sites, our study

also showed that Corbicula typically accumulated higher concentrations of As, Cd, Cu, and Hg,

while E. hopetonensis typically accumulated higher concentrations of Mn and Zn (Table 3.1).

This differs from past studies, which found Corbicula to accumulate significantly higher

concentrations of Pb, while accumulating similar amounts of Cd and Zn to various native species

in Oklahoma (Angelo et al. 2007) or accumulating similar concentrations of Cd, Hg, and Zn as

other bivalves in the Columbia River estuary, Oregon (Caldwell and Buhler 1983). In both of

these studies, multiple species were averaged together, with few of the samples being from

Elliptio species, a fact that may account for this discrepancy. Our comparison of Pb

accumulation was also hampered by difficulty in quantifying concentrations of Pb in Corbicula.

This may be due to the small tissue mass of the organisms or low environmental availability in

the Altamaha system.

Spatial distribution of trace elements

Comparing sites to those immediately upstream showed no underlying longitudinal trend

in trace element bioaccumulation within the system (Figure 3.4). A few trends can be seen. The

site OMG3 tended to show significantly lower levels of bioaccumulation than OMG2, the site

directly upstream (Figure 3.4). Likewise, the site OCN2 showed significantly lower

concentrations of several trace elements than its nearest upstream neighbor, OCN1. Both of

56

these sites (OMG3 and OMG2) were situated in marginal habitat, with specimens that were on

average smaller than those of the nearest neighboring sites.

Abaychi and Mustafa (1988) reported smaller clams collected in the field had

accumulated significantly higher concentrations of Cu, Ni, Pb, and Zn, while larger clams

accumulated significantly higher concentrations of Cd. Luoma et al. 1990 reported Cd, Cr, and

Cu concentrations were positively correlated with Corbicula length. No mean size metrics were

correlated with tissue metal concentrations of Cd, Cu, Hg, and Pb in either species in our study,

although E. hopetonensis displayed a positive correlation between shell length and Cr

concentration (Table 3.2). Because each of these previous studies assigned individuals to size

classes that were then analyzed in composite, the observed correlation between accumulation and

size (in both cases shell length) could be an artifact of aggregation. Conversely, by selecting

individuals of representative sizes for analysis and then averaging by site, we may be unable to

observe the effects of size. Significantly lower accumulation could also be the result of more

frequent valve closures to survive within marginal habitats. Valve closure has been shown to be

a common response in Corbicula to toxic trace elements that limits exposure and deleterious

effects (Doherty et al. 1987).

Also of interest is the site on the Ohoopee, OHP1, which typically showed significantly

higher concentrations of elements than the nearest downstream site, ALT4 (Figure 3.4). Further,

ALT4 frequently had significantly higher concentrations of these elements than its downstream

neighbor, ALT5 and showed significantly higher bioaccumulation than the nearest site upstream

ALT3. This may provide evidence of a source of trace metals (particularly Cd and Hg) within

the Ohoopee drainage that is then transferred to the Altamaha. It may also indicate that the water

57

chemistry parameters (low alkalinity and hardness) of the Ohoopee may increase bioavailability

and therefore accumulation of these trace elements.

One difficulty in using biomonitor organisms to assess the distribution of contaminants is

that in natural environments, a large amount of habitat heterogeneity can exist in a small area,

leading to fine spatial differences in the conditions under which bioaccumulation takes place.

This leads to the comparison of tissue trace element concentrations from ALT1 and ALT2

(Figure 3.4). These sites are ~500 m apart and were collected on the same day. Corbicula at

ALT2 had significantly higher concentrations of As, Cd, Cu, Hg, and Zn than those at ALT1.

ALT1 was a swifter flowing site of packed sand, while ALT2 was a shallower site with little

current, whose sediment was silty sand and dead bivalve shells. The differences in

environmental conditions between sites could have led to the observed differences in trace

element accumulation. This illustrates the kind of fine scale spatial variability that makes it

difficult to draw conclusions on anthropogenic sources of trace elements based on

upstream/downstream comparisons.

Correlation between bioaccumulation and environmental parameters

Bioaccumulation of trace elements by both species was significantly correlated to a

variety of environmental factors (Table 3.2). Two factors frequently negatively correlated with

bioaccumulation were water hardness and alkalinity (e.g. Cd, Cu, Hg, Pb, Zn). With lower

alkalinity and hardness, trace metal concentrations in tissues were found to be higher. This

agrees with predictions of the biotic ligand model (BLM), in which Ca and Mg ions compete

with metals for uptake (Paquin et al. 2002). As discussed earlier, this is important when

considering the reasons for the high metal concentrations found at the site of OHP1, which also

had lower water alkalinity and hardness than all other sites (data not shown).

58

Environmental concentrations of trace metals were correlated with tissue concentrations

only in some instances (Table 3.2). It has been reported that Corbicula accumulate very low

amounts of trace metals from sediment (Doherty 1990). However, the trace elements Cu and Pb

were significantly correlated between concentrations in Corbicula tissues and sediment, while

Cd and Pb were correlated between concentrations in E. hopetonensis tissues and in sediment.

Elder and Mattraw (1984) found no correlation between Corbicula accumulation of Cd, Cr, Cu,

Pb, or Zn and sediment from sites in the Apalachicola River in Florida, although they did find a

correlation with As. However, they analyzed very fine clay particles (<23-μm) rather than the

more inclusive fine fraction we analyzed (<105-μm). Also, their methods resulted in limited

detection of As and Cd from Corbicula tissue, which may have skewed their results. Tatem

(1986) found that Corbicula did not accumulate Cd from contaminated sediment, although they

did find an accumulation of Pb, as did this study. Luoma et al. (1990) using methods similar to

those used in this study found strong correlations between concentrations of Cu, Cd, Pb, and Zn

in Corbicula and fine (<100-μm) sediments sampled from Suisun Bay, California. However, the

correlation was only significant when monthly data were aggregated on a yearly or tri-yearly

basis. In any case, the findings of this study indicate that Corbicula may take up some trace

elements from the fine fraction of sediment it inhabits. Similarly, the native mussel E.

hopetonensis may be accumulating certain toxic trace elements (Cd, Pb) from exposure via fine

sediment sources. Future biomonitoring programs would be advised to consider incorporating

sediment into any sampling protocol.

Some past studies (Abaychi and Mustafa 1988, Luoma et al. 1990, Angelo et al. 2007)

have indicated that trace element tissue concentrations in Corbicula, as well as other bivalves

(Cain and Luoma 1990) fluctuate on a seasonal basis. These changes have been attributed to

59

increased water flow and trace element exposure during seasonal high flows (Abaychi and

Mustafa 1988) and seasonal changes in body mass resulting from growth and reproductive

strategies (Luoma et al. 1990). Our data shows significant negative correlation between mean E.

hopetonensis tissue concentrations of the essential elements Ni and Zn with water temperature,

an indicator of season, while only Zn shows such a correlation in Corbicula (Table 3.2). In this

instance, it seems that essential elements may be more greatly affected by seasonal changes in

the local environment. One proposed approach to eliminating the seasonal changes of tissue

mass that can influence concentration is to calculate body burden as opposed to tissue

concentration (Luoma et al. 1990). However, body burdens of Cd, Cr, Cu, Mn, Ni and Zn are all

significantly correlated to temperature in E. hopetonensis, while no element body burdens are

correlated in Corbicula. Body burden is also more often significantly correlated to organism size

metrics, causing small differences in body size to bias data. Therefore, body burden is not

necessarily a robust metric to use when comparing sites sampled on different dates. The work of

Angelo et al. (2007) concluded that seasonal effects on Corbicula accumulation were secondary

to the effects of anthropogenic inputs into the system. Our study supports this conclusion, which

suggests that sampling season should be a secondary concern when instituting a spatially

intensive biomonitoring program.

Conclusions

This study has provided evidence that the Asian clam Corbicula can be used as a

biomonitoring proxy of trace element accumulation for the native freshwater mussel E.

hopetonensis and adds to previous evidence that Corbicula is a suitable biomonitor of trace

elements. This study also provides the first survey of trace metal accumulation in the bivalves of

the Altamaha River system and indicates some areas that may exhibit increased exposure to trace

60

elements, either through anthropogenic activity or through increased bioavailability caused by

differences in water chemistry. Clearly, more intensive sampling needs to be conducted in this

system to explore the distribution and potential impacts of trace elements on native mussel

assemblages. This study shows that both of the bivalves studied, Corbicula and E. hopetonensis,

may accumulate trace elements from the fine fraction of sediment, suggesting that analysis of

sediment for trace elements should be included in any biomonitoring program. Finally,

correlations between tissue concentrations of trace elements in both species and environmental

factors indicate that local heterogeneity in water chemistry, habitat and organism size play

important rolls and bioaccumulation and should be taken into account when analyzing

bioaccumulation data.

Acknowledgments

I would like to acknowledge Jason Meador, Becky Fauver, Miles Buzbee, Keith Hastie

and Scott Small for their help in field sampling during the course of this study. I’d also like to

thank Diane Addis and Gretchen Loeffler-Peltier for help in processing samples. This study was

conducted with financial support from the US Fish & Wildlife Service as well as the University

of Georgia.

61

Table 3.1. Mean concentration of each element as it was accumulated by each species over all sites. The number of sites in which a comparison between species was possible is given and the number of sites at which each species accumulated significantly higher concentrations of each element. Element Species Concentration

(μg/g) Number of sites

Significantly higher sites

As Corbicula E. hopetonensis

4.55±0.20 2.34±0.06

14 14 0

Cd Corbicula E. hopetonensis

2.34±0.27 1.27±0.08

14 9 0

Cr Corbicula E. hopetonensis

4.18±0.40 2.61±0.18

7 4 0

Cu Corbicula E. hopetonensis

67.8±3.8 5.62±0.12

14 14 0

Hg Corbicula E. hopetonensis

1.296±0.195 0.357±0.028

14 10 1

Mn Corbicula E. hopetonensis

50.9±8.3 4098±191

14 0 14

Ni Corbicula E. hopetonensis

3.84±0.67 1.91±0.14

11 5 0

Pb Corbicula E. hopetonensis

0.553±0.082 0.852±0.129

5 1 2

Zn Corbicula E. hopetonensis

122±3 167±15

14 1 6

62

Table 3.2. A list of physical and environmental parameters significantly (p < 0.05) correlated to the mean amounts of each element at each site as calculated by tissue concentration (μg/g) or body burden (g). The individual factors use the following abbreviations: Wat = concentration of the element in the water; Sed = concentration of the element in the sediment; pH; Temp = water temperature; DO = water dissolved oxygen; Alk = water alkalinity; Hard = water hardness; L = mean shell length; H = mean shell height; D = mean shell depth; TM = thickness metric (shell mass / shell length); CI = condition index (tissue dry mass / shell volume). Element Species (+) Correlation with

Concentration

(-) Correlation with Concentration

(+) Correlation with Body Burden

(-) Correlation with Body Burden

As C. fluminea E. hopetonensis

None TM, CI

None None

L, H, D, TM, CI L, H, D, TM, CI

None None

Cd C. fluminea E. hopetonensis

None Sed

Hard Alk, Hard

Sed L, H, D, TM

None Temp

Cr C. fluminea E. hopetonensis

None L, H, TM

None None

L, H, D, TM L, H, D, TM, CI

None Temp, DO

Cu C. fluminea E. hopetonensis

Sed None

Alk, Hard Hard

Wat, Sed, L, H, D, TM L, H, D, TM

None Temp, DO, CI

Hg C. fluminea E. hopetonensis

None None

Alk, Hard Alk, Hard

None H, D, TM

Hard None

Mn C. fluminea E. hopetonensis

None TM

None None

L, H, D H, D, TM, CI

None Temp

Ni C. fluminea E. hopetonensis

None Wat

None Temp, Alk, Hard

None L, H, D, TM, CI

None Temp, DO

Pb C. fluminea E. hopetonensis

Sed Wat, Sed

pH, Alk, Hard Alk, Hard

None Wat, Sed

None Alk, Hard

Zn C. fluminea E. hopetonensis

None H, D, TM, CI

L, H, D, Temp, Alk, Hard Temp

L, H, D, TM, CI H, D, TM, CI

None Temp

63

Figure 3.1. A map of sites sampled within the Altamaha River system. The following abbreviations map sites to the river they were sampled from: OMG = Ocmulgee; OCN = Oconee; OHP = Ohoopee; ALT = Altamaha Mainstem. Figure 3.2. The natural log of mean metal(loid) concentration in the tissue of E. hopetonensis at each site, plotted as a function of the natural log mean concentration in the tissue of Corbicula. All linear regressions are significant (p < 0.05) with the following r2 values: As = 0.5441; Cd = 0.5807; Cu = 0.3444; Hg = 0.7524; Pb = 0.6686. Figure 3.3. The natural log of mean metal(loid) body burden of E. hopetonensis at each site, plotted as a function of the natural log mean body burden of Corbicula. All linear regressions are significant (p < 0.05) with the following r2 values: As = 0.7226; Cd = 0.6303; Cu = 0.5694; Hg = 0.5172; Zn = 0.7962. Figure 3.4. Mean tissue concentrations found in Corbicula and E. hopetonensis of A) As and Cd; B) Cu and Zn; C) Hg and Pb at each site, arranged up to downstream. Values that are significantly (p < 0.05) higher than those of the site directly upstream are indicated with ↑, while those that are significantly lower are indicated by ↓. River convergences are indicated using lines and large arrows. In the cases of significant differences between two upstream sites, two arrows are used.

64

Figure 3.1.

OMG1

OMG2 OMG3

OMG4

OCN1

OCN2

ALT1&2

ALT3

OHP

ALT4

ALT5&6

ALT7

65

Figure 3.2.

LN Corbicula [Metal]

-4 -2 0 2 4 6

LN H

opet

onen

sis [

Met

al]

-3

-2

-1

0

1

2

3Cu

Hg

Pb

Cd

As

66

Figure 3.3.

LN Corbicula [Metal]

-6 -4 -2 0 2 4

LN H

opet

onen

sis [

Met

al]

-4

-2

0

2

4

6

8

10Cu

Hg

Zn

Cd

As

67

Figure 3.4.

OMG1 OMG2 OMG3 OMG4 OCN1 OCN2 ALT1 ALT2 ALT3 OHP1 ALT4 ALT5 ALT6 ALT7

[Met

al] (μg

/g)

0

1

2

3

4

5

6

7As (Corb)As (Hope)Cd (Corb)Cd (Hope)

↓ ↓

↓↓↑

↑

↑

↓↓

↓↑↑

↓

↓↓

↓

↓ ↓ ↓

↑ ↑

↑

↑

↓↑

↓↑↓

↑

A.

OMG1 OMG2 OMG3 OMG4 OCN1 OCN2 ALT1 ALT2 ALT3 OHP1 ALT4 ALT5 ALT6 ALT7

[Met

al] (μg

/g)

0

100

200

300

400

500

600

700Cu (Corb)Cu (Hope)Zn (Corb)Zn (Hope)

↓ ↓ ↓↓↓ ↓

↓↓

↓↓↓

↓

↓

↓↓↑ ↑

↑ ↑

↑ ↑ ↑

↑ ↑

B

68

Figure 3.4 (cont.)

OMG1 OMG2 OMG3 OMG4 OCN1 OCN2 ALT1 ALT2 ALT3 OHP1 ALT4 ALT5 ALT6 ALT7

[Met

al] (μg

/g)

0

2

4

6

8Hg (Corb)Hg (Hope) Pb (Corb) Pb (Hope)

↑ ↓↓ ↓↓

↑

↑

↑ ↑

↓↑

↓↑

↑↑

↓↓ ↓

↓↑

C.

69

References

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Andres, S., M. Baudrimont, Y. Lapaquellerie, F. Ribeyre, N. Maillet, C. Latouche, and A. Boudou. 1999. Field transplantation of the freshwater bivalve Corbicula fluminea along a polymetallic contamination gradient (river Lot, France): I. Geochemical characteristics of the sampling sites and cadmium and zinc bioaccumulation kinetics. Environmental Toxicology and Chemistry 18:2462-2471.

Angelo, R. T., M. S. Cringan, D. L. Chamberlain, A. J. Stahl, S. G. Haslouer, and C. A. Goodrich. 2007. Residual effects of lead and zinc mining on freshwater mussels in the Spring River Basin (Kansas, Missouri, and Oklahoma, USA). Science of the Total Environment 384:467-496.

Baudrimont, M., J. Metivaud, R. MauryBrachet, F. Ribeyre, and A. Boudou. 1997. Bioaccumulation and metallothionein response in the Asiatic clam (Corbicula fluminea) after experimental exposure to cadmium and inorganic mercury. Environmental Toxicology and Chemistry 16:2096-2105.

Cain, D. J. and S. N. Luoma. 1990. Influence of seasonal growth, age, and environmental exposure on Cu and Ag in a bivalve indicator, Macoma balthica, in San-Francisco Bay. Marine Ecology-Progress Series 60:45-55.

Caldwell, R. S. and D. R. Buhler. 1983. Heavy metals in estuarine shellfish from Oregon. Archives of Environmental Contamination and Toxicology 12:15-23.

Couillard, Y., P. G. C. Campbell, J. Pellerinmassicotte, and J. C. Auclair. 1995. Field transplantation of a freshwater bivalve, Pyganodon grandis, across a metal contamination gradient .2. Metallothionein response to Cd and Zn exposure, evidence for cytotoxicity, and links to effects at higher levels of biological organization. Canadian Journal of Fisheries and Aquatic Sciences 52:703-715.

Doherty, F. G. 1990. The Asiatic clam, Corbicula Spp, as a biological monitor in freshwater environments. Environmental Monitoring and Assessment 15:143-181.

Doherty, F. G., D. S. Cherry, and J. Cairns. 1987. Valve closure responses of the Asiatic clam Corbicula fluminea exposed to cadmium and zinc. Hydrobiologia 153:159-167.

70

Doherty, F. G., D. S. Cherry, and J. Cairns. 1990. Multiseasonal tissue-growth trends in Corbicula fluminea (Bivalvia, Corbiculidae) from the New River, Virginia. Nautilus 104:10-15.

Elder, J., Collins, JJ. 1991. Freshwater molluscs as indicators of bioavailability and toxicity of metals in surface-water systems. . Reviews Environ Contam Tox 122:37-79.

Elder, J. F. and H. C. Mattraw. 1984. Accumulation of trace elements, pesticides, and polychlorinated biphenyls in sediments and the clam Corbicula manilensis of the Apalachicola River, Florida. Archives of Environmental Contamination and Toxicology 13:453-469.

Graney, R. L., D. S. Cherry, and J. Cairns. 1983. Heavy metal indicator potential of the Asiatic clam (Corbicula fluminea) in artificial stream systems. Hydrobiologia 102:81-88.

Graney, R. L., D. S. Cherry, and J. Cairns. 1984. The influence of substrate, pH, diet and temperature upon cadmium accumulation in the Asiatic clam (Corbicula fluminea) in laboratory artificial streams. Water Research 18:833-842.

Kesler, D. H. 2004. Influence of a lentic area on condition indices of Corbicula fluminea in the Wolf River, Tennessee. Journal of Freshwater Ecology 19:445-453.

Kraemer, L. R. 1979. Corbicula (Bivalvia, Sphaeriacea) vs indigenous mussels (Bivalvia, Unionacea) in United-States Rivers - A hard case for interspecific competition? American Zoologist 19:1085-1096.

Luoma, S. N., R. Dagovitz, and E. Axtmann. 1990. Temporally intensive study of trace metals in sediments and bivalves from a large river, estuarine system - Suisun Bay Delta in San-Francisco Bay. Science of the Total Environment 97-8:685-712.

Meador, J. R. 2008. The development and evaluation of a freshwater mussel sampling protocol for a large lowland river. University of Georgia, Athens, GA.

Naimo, T. J. 1995. A review of the effects of heavy metals on freshwater mussels. Ecotoxicology 4:341-362.

Paquin, P. R., J. W. Gorsuch, S. Apte, G. E. Batley, K. C. Bowles, P. G. C. Campbell, C. G. Delos, D. M. Di Toro, R. L. Dwyer, F. Galvez, R. W. Gensemer, G. G. Goss, C. Hogstrand, C. R. Janssen, J. C. McGeer, R. B. Naddy, R. C. Playle, R. C. Santore, U.

71

Schneider, W. A. Stubblefield, C. M. Wood, and K. B. Wu. 2002. The biotic ligand model: a historical overview. Comparative Biochemistry and Physiology C-Toxicology & Pharmacology 133:3-35.

Patrick, R. 1994. Rivers of the United States. J. Wiley, New York.

Perceval, O., Y. Couillard, B. Pinel-Alloul, A. Giguere, and P. G. C. Campbell. 2004. Metal-induced stress in bivalves living along a gradient of Cd contamination: relating sub-cellular metal distribution to population-level responses. Aquatic Toxicology 69:327-345.

Pourang, N. 1996. Heavy metal concentrations in surficial sediments and benthic macroinvertebrates from Anzali wetland, Iran. Hydrobiologia 331:53-61.

Reese, E. G. and D. P. Batzer. 2007. Do invertebrate communities in floodplains change predictably along a river's length? Freshwater Biology 52:226-239.

Sickel, J. 1973. A new record of Corbicula manilensis (philippi) in the southern Atlantic slope region of Georgia. . Nautilus 87:11-12.

Stites, D. L., A. C. Benke, and D. M. Gillespie. 1995. Population dynamics, growth, and production of the Asiatic clam, Corbicula fluminea, in a blackwater river. Canadian Journal of Fisheries and Aquatic Sciences 52:425-437.

Tatem, H. E. 1986. Bioaccumulation of polychlorinated biphenyls and metals from contaminated sediment by freshwater prawns, Macrobrachium rosenbergii and Clams, Corbicula fluminea. Archives of Environmental Contamination and Toxicology 15:171-183.

Unrine, J. M., W. A. Hopkins, C. S. Romanek, and B. P. Jackson. 2007. Bioaccumulation of trace elements in omnivorous amphibian larvae: Implications for amphibian health and contaminant transport. Environmental Pollution 149:182-192.

USEPA. 1994. EPA Method 3015. Microwave assisted acid digestion of aqueous samples and extracts. U.S. Environmental Protection Agency, Washington, D.C.

USEPA. 1996. EPA Method 3052. Microwave assisted digestion of siliceous and organically based matrices. Revision 0 (December 1996). U.S. Environmental Protection Agency, Washington, D.C.

72

Villar, C., J. Stripeikis, L. D'Huicque, M. Tudino, O. Troccoli, and C. Bonetto. 1999. Cd, Cu and Zn concentrations in sediments and the invasive bivalves Limnoperna fortunei and Corbicula fluminea at the Rio de la Plata basin, Argentina. Hydrobiologia 416:41-49.

Williams, J. D., M. L. Warren, K. S. Cummings, J. L. Harris, and R. J. Neves. 1993. Conservation status of freshwater mussels of the United States and Canada. Fisheries 18:6-22.

73

Appendix 3.1. Element concentrations (in μg/g) in the tissues of each bivalve species, given as the mean of each site, plus or minus standard error. Underlined values indicate a mean that is significantly higher than the mean for the other species co-occurring at that site. These sites are those on the major tributaries of the Altamaha.

Site Species As Cd Cr Cu Hg Mn Ni Pb Zn C. flum. 6.35±0.31 0.83±0.13 UR 71.7±5.48 0.163±0.031 20.0±4.6 1.50±0.30 0.154±0.019 137±6 OCN1 E. hope. 3.70±0.46 0.84±0.23 UR 6.33±0.51 0.209±0.064 7171±942 1.31±0.19 0.275±0.031 497±187C. flum. 3.05±0.40 1.06±0.09 1.50±0.18 43.2±6.7 0.129±0.016 18.0±1.8 2.02±0.37 0.245±0.015 126±7 OCN2 E. hope. 1.51±0.05 0.27±0.02 0.55±0.07 4.62±0.33 0.142±0.007 2259±190 1.09±0.08 0.134±0.012 132±7 C. flum. 4.85±0.23 4.77±0.20 UR 80.4±8.7 0.566±0.056 81.0±9.5 1.65±0.16 0.429±0.076 169±3 OMG1 E. hope. 1.76±0.24 2.56±0.44 UR 6.47±0.60 0.326±0.040 3901±700 3.67±1.13 0.580±0.106 148±14 C. flum. 3.01±0.35 2.30±0.23 3.11±0.63 34.7±11.6 0.212±0.160 18.2±1.6 9.23±1.86 BD 134±5 OMG2 E. hope. 1.83±0.12 0.86±0.12 3.29±0.58 4.71±0.28 0.298±0.018 2897±327 1.68±0.22 0.312±0.053 125±4 C. flum. 4.09±0.10 2.46±0.16 1.94±0.16 64.8±2.8 0.687±0.026 16.8±2.6 4.25±5.66 BD 104±3 OMG3 E. hope. 2.18±0.11 2.35±0.14 2.45±0.30 6.38±0.22 0.314±0.148 4323±349 1.97±0.10 0.737±0.025 119±6 C. flum. 3.55±0.20 3.05±0.13 2.39±0.15 38.9±4.3 0.381±0.046 64.6±11.3 2.78±0.32 BD 131±3 OMG4 E. hope. 2.04±0.09 1.70±0.18 2.52±0.25 5.50±0.28 0.328±0.017 3148±231 1.94±0.26 0.348±0.031 118±3 C. flum. 4.62±0.13 3.23±0.12 UR 152.1±9.1 7.49±0.54 45.1±11.2 7.77±4.38 1.31±0.188 179±5 OHP1 E. hope. 2.26±0.15 3.39±0.25 UR 8.14±0.41 1.06±0.032 7715±755 3.08±0.21 5.37±0.687 201±9

* Standard error could not be calculated for this element at this site. BD = All samples were below detection for this element at this site. UR = All samples were unreliable for this element at this site.

74

Appendix 3.1 (cont). Element concentrations (in μg/g) in the tissues of each bivalve species, given as the mean of each site, plus or minus standard error. Underlined values indicate a mean that is significantly higher than the mean for the other species co-occurring at that site. These sites are those on the main channel of the Altamaha River.

Site Species As Cd Cr Cu Hg Mn Ni Pb Zn C. flum. 3.89±0.10 1.42±0.09 UR 41.0±3.3 0.543±0.049 41.3±3.9 BD BD 100±4 ALT1 E. hope. 2.41±0.23 1.04±0.20 UR 4.72±0.37 0.307±0.026 3473±543 1.17±0.27 0.268±0.022 157±16 C. flum. 5.88±0.49 2.05±0.24 UR 83.8±19.7 2.776±0.671 105±23.3 BD BD 122±6 ALT2 E. hope. 2.84±0.19 0.94±0.15 UR 5.45±0.41 0.620±0.304 4753±761 0.94±0.19 0.266±0.041 171±14 C. flum. 4.68±0.26 2.00±0.17 8.46±0.63 43.4±3.2 0.318±0.054 35.8±2.0 5.61±0.59 BD 138±7 ALT3 E. hope. 2.39±0.10 0.43±0.02 1.73±0.09 6.78±0.34 0.217±0.011 4061±212 2.30±0.42 0.281±0.049 147±5 C. flum. 6.22±2.45 6.37±3.70 4.95±1.20 63.0±9.5 1.456±0.175 19.2±1.0 3.19±0.56 0.608* 144±9 ALT4 E. hope. 2.18±0.09 1.39±0.22 2.35±0.39 5.37±0.21 0.492±0.031 4318±497 1.53±0.13 0.750±0.063 150±6 C. flum. 3.05±0.26 0.78±0.18 UR 39.8±6.1 0.529±0.147 30.7±10.5 BD 0.322* 60.9±2 ALT5 E. hope. 2.28±0.21 0.35±0.03 UR 4.50±0.34 0.191±0.023 1358±217 1.25±0.39 0.200±0.030 103±9 C. flum. 4.75±0.82 1.32±0.19 UR 97.8±16.9 0.787±0.156 151±96.1 2.59* 0.589* 71.8±9 ALT6 E. hope. 2.30±0.21 0.49±0.06 UR 4.90±0.34 0.187±0.025 2141±723 0.86±0.20 0.313±0.063 112±16 C. flum. 5.20±0.15 1.67±0.10 5.59±0.60 67.4±2.9 0.491±0.049 31.0±3.9 3.22±0.30 0.291±0.301 112±3 ALT7 E. hope. 2.92±0.16 1.06±0.27 2.96±0.31 4.73±0.23 0.282±0.014 5619±661 1.43±0.11 0.277±0.036 154±10

* Standard error could not be calculated for this element at this site. BD = All samples were below detection for this element at this site. UR = All samples were unreliable for this element at this site.

75

CHAPTER FOUR

THE ASIAN CLAM CORBICULA FLUMINEA AS A BIOMONITOR OF TRACE ELEMENT

CONTAMINATION: ACCOUNTING FOR NATURAL VARIATION WHEN IDENTIFYING

ANTHROPOGENIC SOURCES1

Abstract

Widespread decline of freshwater mussel populations has led to interest in monitoring

anthropogenic contaminants that could potentially be harming native mussel populations. In this

study, specimens of the invasive clam, Corbicula fluminea, were collected above and below

possible sources of potentially toxic trace elements (As, Cd, Cr, Cu, Hg, Pb and Zn) in the

Altamaha River System. Their bioaccumulation of these elements was quantified, along with

environmental (water and sediment) concentrations. Hierarchical linear models were used to

decrease variability in concentrations related to environmental (site water chemistry and

sediment characteristics) and individual (growth metrics) variables. This study found

significantly elevated bioaccumulation of Cd, Cu and Hg below the outfall of kaolin processing

facilities, Zn below a tire cording facility, and Cr below a nuclear power plant and a paper pulp

mill. Hierarchical linear models found correlations between trace element accumulation and

variables such as upstream distance, dissolved oxygen, % silt and clay in the sediment, elemental

sediment concentration, shell length and bivalve condition index. Taking into account this

variability reduced the number of sites with significantly elevated trace elements. This finding

1 Shoults-Wilson WA, Peterson JT, Unrine JM, Rickard J, Black MC. To be submitted to Environmental Toxicology and Chemistry.

76

shows that conclusions about trace element distribution cannot be drawn from bioaccumulation

studies without taking into account environmental and individual sources of variation.

Introduction

An unfortunate recent development in aquatic ecology has been the widespread decline of

native mussel populations throughout the United States (Williams et al. 1993). There are

numerous possible explanations for these declines, including habitat alteration from

sedimentation and impoundment, changes in fish-host populations and distributions, competition

from invasive species and exposure to anthropogenic toxicants (Williams et al. 1993, Naimo

1995, Vaughn and Taylor 1999).

Toxic trace elements such as arsenic (As), cadmium (Cd), chromium (Cr) copper (Cu),

mercury (Hg), lead (Pb) and zinc (Zn) have been shown to exert acute and chronic toxic effects

on various freshwater bivalves (Elder 1991, Naimo 1995). Responses can include altered valve

movement (Doherty et al. 1987), inhibition of growth (Belanger et al. 1986), reduced fecundity

(Perceval et al. 2004) and mortality (Harrison et al. 1984, Elder 1991, Naimo 1995). Because of

their ability to accumulate trace elements following exposure, freshwater bivalves, particularly

the invasive Asian clam Corbicula fluminea (hereafter Corbicula), are frequently used as

biological monitors of trace elements contamination (Millington 1983, Abaychi and Mustafa

1988, Doherty 1990, Luoma et al. 1990, Pourang 1996, Gundacker 2000, Tomazelli et al. 2003,

Sebesvari et al. 2005, Angelo et al. 2007).

In biomonitoring studies, an increased concentration of trace elements in the tissues of

the biomonitor species is typically considered an indicator of increased environmental

availability of that element. However, a host of environmental factors that include size and age

(MetcalfeSmith et al. 1996), sampling season (Luoma et al. 1990) and the chemistry of

77

surrounding water and sediment (Graney et al. 1984, Angelo et al. 2007) have all been shown to

be correlated to the bioaccumulation of various trace elements. This makes it difficult to draw

conclusions about contaminant levels based solely on tissue concentrations of trace elements in

the biomonitor organism.

The Altamaha River system is the largest drainage basin encompassed by the state of

Georgia and is home to 3 imperiled native mussel species (Meador 2008). The Oconee River, a

primary tributary of the Altamaha is also home to an extensive kaolin mining and processing

industry (Pruett 2000). The kaolin industry in Georgia and abroad, has been implicated for

increasing loads of Zn and Hg in local sediments (Jordao et al. 2002, Lasier et al. 2004), with

potential detrimental implications to native mussels. Other potential point sources for trace

elements in this region include a former tire cording plant, which has been fined for excessive

discharge of Zn and Cu, a nuclear power plant, a paper mill and a state prison. The area is also

subject to non-point sources, although its lower reaches have only small urban areas and little

light industry.

This study examined 1) whether proximity to various industrial activities increases the

concentrations of trace elements in resident bivalves; 2) the utility of hierarchical linear

modeling in accounting for variability in trace elements accumulation; and 3) the amount of

variability in elemental concentrations that could be accounted for by environmental factors or

individual growth rather than human activity. This would not only indicate sources of stressors

to bivalves in the Altamaha River but it would also provide a more accurate indication of human

influence on trace element accumulation.

78

Materials and Methods

Study Area

The Altamaha River drainage is completely contained within the state of Georgia, USA.

The mainstem of the Altamaha River is formed by the confluence of the Ocmulgee and Oconee

Rivers, whose headwaters are in the piedmont phyto-geographic province and cross the fall line

at Macon and Milledgeville GA, respectively (Reese and Batzer 2007). Study sites were chosen

based on potential point sources of trace elements and to provide coverage of the majority of the

watershed area.

Twenty-two sites were chosen for this study, with sites located upstream and downstream

of 6 potential point sources of trace elements: Little Commissioner’s Creek (LCC) which carries

effluent from a kaolin processing facility, Kettle Creek (KC) which drains an area with active

kaolin mines, the former Amercord tire facility (AMC) on the Ocmulgee River, Plant Hatch

Nuclear Power Station (PH) and the Rayonier paper mill (RAY) on the Altamaha River and the

Reidsville State Penitentiary (RSP) on the Ohoopee River (Figure 4.1). The remaining 10 sites

were spread out among the rivers and their tributaries, with emphasis placed on the Oconee

because of the large amount of kaolin mines within its drainage. Habitats sampled included

depositional sloughs, edges of sandbars, rocky shelves of the unique “Altamaha grit”

microhabitat and gravel seams in the main channel. GIS coordinates were taken and recorded for

each site at the time of sampling (Magellan explorist 210; Thales Navigation, San Diego CA),

and a sketch of the site was made indicating major features of substrate and local morphology.

Sampling occurred during the spring and early summer of 2007.

79

Sample Collection

Sample collection is described in detail in Chapter 3. Briefly, water temperature,

dissolved oxygen, pH and conductivity were determined on site, while water alkalinity and

hardness were determined later in the laboratory. Grab samples of water (~500 mL), sediment

(~500 mL) and Corbicula (5-35 individuals) were collected at each site. A petite ponar grab was

used to collect sediment and was also used to collect Corbicula during high flows. All samples

were kept in a cooler that contained small amounts of ice until they could be transported back to

the laboratory.

Sample Processing

Water and Corbicula processing are described in more detail in Chapter 3. Water

samples were filtered (0.5 μm filter) and acidified (10%; trace metal grade HNO3). Corbicula

were measured for length, height, depth and shell mass. Whole tissues were removed from

shells, dried, massed and stored in acid-washed vessels. Shell valves were measured for volume

as described in Kesler (2004).

Sediment samples were split into two separate sub-samples. One sub-sample was re-

homogenized and poured through a 1 mm acid-washed plastic sieve. The resulting < 1 mm

fractions were placed in acid-washed nalgene containers and stored at –20°C. Frozen samples

were thawed under refrigeration at a later date and wet-sieved again through a 105 μm Nytex

mesh (Spectrum Mesh; Rancho Dominguez CA, USA) into an acid-washed 600 mL beaker as in

(Luoma et al. 1990). Fine fractions were dried at temperatures ranging from room temperature

to 50°C, homogenized using a dry, acid-washed mortar and pestle and stored in acid washed

centrifuge tubes under anhydrous conditions until digestion.

80

The other sub-sample was refrigerated until it could be homogenized and dried. It was

then put through nested sieves (4 mm, 2 mm, 1 mm, 500 μm, 250 μm, 125 μm and 64 μm mesh)

as recommended by Plumb (1981). These sediment fractions were carefully transferred to weigh

boats and massed to determine the percentage of each size fraction in the overall sample. At

least 98% of the original material was accounted for.

Acid-Digestion

Between 0.25 and 0.5 g of dried fine fraction sediment were completely digested in 8 mL

trace metal grade HNO3 and 3 mL HF, using a MARS 5 HP 500 Plus system (CEM Corp;

Matthews NC, USA), under the conditions of EPA protocol 3052 (USEPA 1996). Three

replicate samples were digested for each of the sediment composites. All digestions were diluted

in 24 mL of Milli-Q water to ~33% acid. Blanks and standard sediments (MESS-3, National

Research Council of Canada, Ottawa ON, Canada; NIST 1645, National Bureau of Standards,

Gaithersburg MD, USA) were also digested with each run. All samples, blanks and standards

were stored at room temperature in acid-washed centrifuge tubes until analysis.

Whole tissues of Corbicula were completely digested in 5 mL of trace metal grade

HNO3, using a MARS 5 HP 500 Plus system (CEM Corp; Matthews NC, USA), under the

conditions of EPA protocol 3015 (USEPA 1994). Digestions were diluted with 10 mL Milli-Q

water to ~33% acid. Exact concentrations were calculated using dry tissue mass and the mass of

the final solution. One blank (acid only) and one standard reference material (SRM) of ~0.075 g

TORT-2 lobster hepatopancreas (National Research Council of Canada, Ottawa ON, Canada)

were digested in each run. Dilute samples, blanks and standard were stored at room temperature

until analysis.

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Sample Analysis

Analyses of digested samples were carried out as described in Unrine et al. (2007). Trace

element concentrations in digests (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb and Zn) were quantified using

a Perkin-Elmer Sciex Elan DRC Plus inductively coupled plasma-mass spectrometer (ICP-MS;

Norwalk CT, USA) operating in standard mode. Method detection limits (MDLs) were

calculated using blank digests. Mean MDLs ranged from 0.0250 μg/g for Cd to 3.360 μg/g for

Cu. Recoveries of SRMs were in good agreement with certified values, mean recoveries ranging

from 85.27% for As and 130.04% for Pb. Relative percent difference (RPD) of replicate

dilutions of sub-samples was used to approximate analytical error. On average (n=34) RPD

ranged from 2.01% for As and Zn to 12.80% for Pb. Spikes of ~2-5 times the un-spiked sample

concentration had mean recoveries ranging from 91.61% for Cr and 101.87% for Ni (n=33).

Statistical Analysis

Initially, analysis of variance (ANOVA) followed by Duncan’s post hoc test were

calculated using Statistical Analysis Software (SAS v9.1, SAS Institute, Cary NC, USA). These

tests determine significant (α = 0.05) differences between metal concentrations in clams

collected both upstream and downstream of potential trace element sources (Fig. 4.1).

In order to reduce variation in the data caused by natural factors, we employed

hierarchical linear models. Hierarchical linear models are able to reduce variation at multiple

levels of biological organization, such as the individual level, the site (i.e. local environment)

level, the regional (e.g. watershed) level, etc. By reducing naturally occurring variation, this

approach can then test what differences in bioaccumulation are more likely due to human

82

activities. This approach also allows multiple models to be applied to the data and ranked for

their usefulness (Burnham and Anderson 2001).

In our approach to limiting natural variation in bioaccumulation, we used predictors at

two levels of biological organization. Individual (Level-1) predictors were: shell length, height

and depth; shell thickness index [shell mass / shell length (Griffiths and Cyr 2006)]; condition

index [tissue dry weight / shell volume; (Kesler 2004)]; tissue dry weight. Site specific (Level-2)

predictors were: latitude, longitude and upstream distance from the estuary; day of the year

collected; water temperature, DO, pH, conductivity, alkalinity and hardness; percent sediment

fraction (silts/clays, very fine sand, fine sand, medium sand, coarse sand, very coarse sand, fine

gravel, gravel); sediment sorting index (standard deviation of sediment fractions); sediment

coarseness index (weighted average of sediment fractions); and water and sediment

concentrations of the element being modeled. Incorporating Level-1 predictors into a model

decreases variability within a site, while incorporating Level-2 predictors into a model decreases

variability between sites.

All level-2 predictors were considered singly and the decrease in inter-site variation

calculated. All combinations of the Level-2 predictors that decreased inter-site variation were

then considered in two-variable models unless they were correlated to each other (Pearson’s

coefficient squared < 0.30). Level-1 predictors were then considered singly and the decrease in

intra-site variation calculated for each. Fully hierarchical models of 1 Level-1 predictor and 1

Level-2 predictor were also considered for all Level-1 predictors, combined with most Level-2

predictors (only the 3 sediment fractions which decreased intra-site variation the most and

%silt/clay were considered in this manner).

83

The Aikake Information Criteria (AIC) were calculated for all models using the following

formula:

AIC = -2*ln(likelihood) + 2*K

Where K = the number of parameters in the model (Burnham and Anderson 2001).

Data sets where n/K < 40 had AICs bias-adjusted using the following formula:

AICC = -2*ln(likelihood) + 2*K + [2*K*(K+1)]/(n-K-1)

AIC values were used to rank models, with the model with the lowest AIC being the best

fitting model. Subtracting the lowest AIC from all AICs resulted in ranked Δi values, such that

the best fitting model had Δi = 0. Finally Aikake weights were calculated to quantify the

plausibility of the model, using the following equation:

wi = __exp(-0.5*Δi)__ ΣR

r=1 exp(-0.5*Δr)

Only models in which wi(Δi>0) > (wi(Δi=0) / 2) were considered for the selected model. In

other words, only models for which the best-fit model was less than twice as likely were

considered. A final model for each element was selected based on low Aikake weights, the

environmental importance of the predictors used in the model and the ability to make

up/downstream comparisons using the model (see below).

Using the final models to take into account inter- and/or intra-site variability, the

difference of the intercepts (i.e. mean tissue concentrations after the model is fit) for all the

up/downstream pairs was calculated. The 95% confidence intervals of all difference were also

calculated. Significantly non-zero (p < 0.05) differences were those whose 95% confidence

intervals did not overlap zero. A non-zero difference indicated that the mean concentrations of

the up/downstream pair were significantly different.

84

Results

Mean concentrations of elements in all three compartments (water, sediment, Corbicula

tissues) are compiled in Appendix 4.1. The up/downstream comparisons of mean tissue

concentrations made using ANOVA with Duncan’s post hoc test are represented in Figure 4.2.

Of the six up/downstream comparisons, the site downstream of LCC is the only one that

consistently showed significantly (p < 0.05) higher tissue concentrations of trace elements (Cd,

Cu, Hg, and Zn) than in the tissues of clams collected from the site upstream (Figure 4.2). All

other sites demonstrated no definitive trends between elements, showing significantly higher

concentrations in clams for one or more element both upstream and down.

Best fitting hierarchical linear models for each element are given in Table 4.1 with AIC,

Δi and wi values. The final hierarchical models selected for each element are given in Table 4.2

with estimates for each parameter and an estimation of the reduction in variability resulting from

using the model. Models with Δi > 0 were chosen over models with Δi = 0 when wi(Δi>0) >

(wi(Δi=0) / 2) and when their model parameters were deemed more environmentally relevant. For

instance, upstream distance was considered to be more environmentally relevant to stream spatial

organization than longitude or latitude and was preferred to these two (As and Cr models; Table

4.1). In one instance (Cd model) a final model in which Δi > 0 was selected because the model

in which Δi = 0 could not calculate all up/downstream comparisons (Table 4.1).

The differences in the final model intercepts (mean concentrations of elements) between

clam samples at each up/downstream pair are given in Figure 4.3. Note that the 95% confidence

interval (CI95) displayed in this graph provides a visual indication of significant differences. If

the CI95 overlaps with zero, that indicates there is no difference between the concentrations

found at each site after the final model reduces variability.

85

Discussion

Applying the hierarchical linear models to the bioaccumulation data from the 22 sites

reduced overall variability in the data to varying degrees (Table 4.2). When these models were

used to assess six up/downstream comparisons, fewer significant differences between sites were

found than when the tissue concentrations of these sites were compared directly. In this section,

we will discuss the effects of the model on our conclusions and how they relate to previous

studies.

Arsenic (As)

When ANOVA was used to compare the six up/downstream comparisons in our study,

two were found to be significantly different, indicating higher exposure to As at one site rather

than the other (Figure 4.2B). Following the selection of a final model (Table 4.1 & 4.2),

variability related to DO and upstream distance was controlled and the resulting comparisons

revealed no significant differences between any up/downstream pair (Figure 4.3B).

Few studies have used Corbicula as a biomonitor species for As, although past work has

shown no relation between As accumulation and the length or shell weight of organisms

(Sebesvari et al. 2005). This agrees with our finding that organism growth metrics did not

explain As accumulation in Corbicula. On the other hand, our findings indicate that DO

influences As accumulation, with clams collected from more oxygenated surface waters

portending higher accumulation. This increased accumulation may be due to increased

availability of the arsenate ion, the dominant form of As under oxic conditions. It could also be

related to a seasonal fluctuation in As accumulation, since higher DO accompanies higher flows

and colder temperatures. The selected model also incorporated stream distance to decrease inter-

site variability, with upstream sites tending to show lower levels of bioaccumulation. The sites

86

that we sampled on smaller, blackwater tributaries tended to be located further upstream, while

sites sampled on river mainstems were on average located further downstream. Thus, these

differences in accumulation could be attributed to differences in the chemistry between small,

blackwater tributaries and main river channels. In either case, it appears that variations in As

bioaccumulation at these site pairs can be primarily attributed to environmental differences at

each site rather than human activity.

Cadmium (Cd)

Using ANOVA, Corbicula collected from a site directly downstream of LCC were found

to have accumulated significantly higher concentrations, while the reverse was true for sites

above and below RAY (Figure 4.2B). However, using a linear hierarchical model incorporating

the length of individual clams and the % of silt and clay (<64 μm) particles at each site, the

difference between the RAY sites was no longer significant at α = 0.05 (Figure 4.3B).

These Cd data illustrate the importance of taking into account environmental conditions

at each site before attributing significant differences to human activities. The site upstream of

RAY was predominantly very coarse sand (47.85%) with little silt/clay (1.26%). Meanwhile, the

downstream site was located in a slough with predominantly very fine sand (49.16%) and a large

amount of silt/clay (16.28%). Past studies using artificial streams have found that Corbicula

accumulate significantly less Cd in a predominantly silt/clay system than in systems that were

predominantly or completely sandy in composition (Graney et al. 1984). Past studies also

support our finding that longer Corbicula accumulate higher concentrations of Cd than smaller

individuals (Abaychi and Mustafa 1988, Metcalfe-Smith et al. 1996, Angelo et al. 2007).

However, even with the model taking into account some of the variation in observed

concentrations, clams collected from the site below LCC accumulated significantly higher Cd

87

concentrations than the upstream site. This indicates that effluent from the kaolin-processing

plant or another local industry may be introducing bioavailable Cd to Commissioner’s Creek (an

Oconee tributary) via LCC. This is somewhat surprising, given that sediments potentially

impacted by kaolin processing activity typically show concentrations of Cd below detection

(Jordao et al. 2002, Lasier et al. 2004). The concentrations (Appendix 4.1) of Cd found in

sediments from our study sites on the Oconee were 1-2 orders of magnitude greater than those

found by Lasier et al. in the same area in 1998, using similar methodos. This may indicate a new

source of Cd in the area or a change in industrial practices.

Chromium (Cr)

Clams sampled at sites downstream of both the Plant Hatch (PH) nuclear power facility

and Rayonier paper mill (RAY) accumulated significantly higher concentrations of Cr than their

upstream counterparts when compared using ANOVA (Figure 4.2B). Subsequent hierarchical

modeling resulted in a model using organism condition and stream distance as the primary

controls for variability, such that Corbicula with a better condition that were located further

downstream, accumulated greater concentrations of Cr. Even after the model took into account

the variation caused by condition and stream distance, significant differences were still found

between the paired sites at PH and RAY (Figure 4.3B).

Past studies have shown a correlation between Cr accumulation in Corbicula and

organism size, the season of collection and sediment loads of Cr (Luoma et al. 1990). In our

study, indicators of season (temperature, DO, day of year) and [Cr]Sediment did not produce one of

the best-fit models. Condition of the organism [tissue dry weight / shell volume; (Kesler 2004)]

was used in the selected model, with organisms in poorer condition showing higher accumulation

of Cr. Because dry weights of Corbicula can fluctuate seasonally (Luoma et al. 1990), this may

88

indicate some seasonal fluctuation in Cr accumulation. As with As, organisms in smaller

tributaries tended to accumulate lower concentrations of Cr, probably leading to the influence of

stream distance.

The significant difference between organisms above and below PH is most likely due to

the fact that the nuclear power station is a permitted discharger of Cr and Zn (USEPA 2006) and

elevated tissue concentrations of these elements may be expected. RAY, a paper fibers mill, has

permits to discharge Hg, Mn, Ni, Pb, V and Zn but not Cr (USEPA 2006). However, Cr has

been found bound to a potentially bioavailable fraction of pulp and paper waste from a mill in

Finland (Poykio et al. 2007). It is also closely associated with the ferro-manganese oxide (FMO)

fraction of freshwater sediment (Koretsky et al. 2006). By releasing large amounts of Mn

compounds [110000 lbs/year; (USEPA 2006)], the mill may be increasing bioavailability of Cr.

Copper (Cu)

ANOVA comparisons indicated significant differences between two site pairs (AMC and

LCC; Figure 4.2A). Model selection resulted in a best-fit model using shell length and upstream

distance as the variables. After controlling for these variables, both differences were still found

to be significant, while a third site pair was found to be significantly different (RSP; Figure

4.3A).

Like Cr, Cu has also been reported to vary with organism size, season, and sediment

concentrations of the metal (Luoma et al. 1990). Also like Cr, our best-fit model used stream

distance along with a growth metric (length) to reduce variation. With respect to the site pairs

showing significant differences, the site downstream of LCC showed significantly higher

concentrations of Cu. Sediment in the Oconee River has been shown to be enriched with Cu

(Lasier et al. 2004) but the authors could not link these levels to kaolin activities. Our findings

89

indicate a source of Cu emanating from LCC that causes increased concentrations of Cu in

downstream organisms. Copper readily sorbs to kaolin particles, with increased adsorption in

the presence of humic acids (Arias et al. 2002) and neutral pH (Katsumata et al. 2003) such as

can be found in some blackwater systems (Patrick 1994). Because bivalves preferentially filter

fine particles (Vaughn and Hakenkamp 2001), the presence of kaolin particles in solution could

be mobilizing copper and other metals. This is in agreement with some of the conclusions of the

biodynamic model of trace element bioaccumulation, which states that assimilation from food is

the primary source of metals to aquatic organisms (Luoma and Rainbow 2005). This has been

found to be true for Cu accumulation in Corbicula in particular (Croteau et al. 2004, Croteau and

Luoma 2005).

The other two site pairs (AMC and RSP) had upstream sites with significantly higher

concentrations of Cu. In the case of AMC, the former Amercord tire plant was fined for illegal

release of Cu compounds (ARK 2003), which would imply that organisms below its effluent

release may show increased accumulation. The Cu compounds released by the Amercord plant

apparently did not have a significant impact on accumulation at this site at the time of sampling.

Operations ceased at this plant in 2001, so discharges since that time have been limited to rain

overflow of chemical storage tanks (Spinrad 2003). This may mean that Cu at this site has been

diluted or sequestered with time, limiting the bioaccumulation that we observed, although there

was no difference in the Cu concentrations found in the sediment (Appendix 4.1). Sediment

sequestration of metals can occur fairly quickly and while it may decrease exposure in the short

term, contaminated sediment may serve as a long-term source (Salomons et al. 1987, Foster and

Charlesworth 1996).

90

The last site with significant differences after the model was taken into account (RSP) is

unique in being the only site whose difference was not shown to be significant using ANOVA.

The reason our linear hierarchical model indicated that the differences were significant was

because the downstream organisms were on average larger, while mean concentrations of Cu

were similar between the two sites. Controlling for shell length accentuated the slight difference

between the two sites. In other words, the upstream organisms accumulated higher levels of Cu

despite being smaller than the downstream organisms. Because Cu accumulation normally

increases with length, this small difference in concentration becomes important.

Mercury (Hg)

Of our six site pairs compared using ANOVA, three upstream sites were found to have

significantly higher levels of bioaccumulation of Hg than their downstream counterpart, while

one downstream site had significantly higher Hg concentrations (Figure 4.2C). The best-fit

linear hierarchical model for Hg used only the variable of shell length. Past studies have also

found bivalves with longer shells to accumulate higher concentrations of Hg (MetcalfeSmith et

al. 1996). Because larger individuals are older (Stites et al. 1995), this is probably due to fact

that the methylated form of Hg is readily accumulated in Corbicula tissues over time, with much

slower elimination that inorganic Hg (Inza et al. 1998). Older individuals will have more time to

accumulate Hg but will be unable to eliminate it.

After taking into account organism length, only two up/downstream pairs showed

significant differences in Hg bioaccumulation (Figure 4.3C). One pair consisted of the sites

above and below KC. In this case, the upstream site showed higher concentrations. This

probably reflects elevated methylation that takes place in wetlands (Rudd 1995), especially

humic-rich, blackwater wetlands (Jagoe et al. 1998), such as those that make up the riparian zone

91

of Buffalo Creek, where the sites were located. A recent survey of trace elements accumulated

by Corbicula in the Chattahoochee and Broad Rivers (Peltier et al. 2008) found the highest Hg

accumulation in catchments with higher percentages of forest cover. The authors attributed this

to the greater organic materials associated with forested catchments.

The other pair were again the sites above and below LCC. The downstream clams

accumulated significantly more Hg than the upstream site. This is not surprising, considering

that Lasier et al. (2004) found that sediments in the Oconee River directly below the confluence

with Commissioner’s Creek had concentrations of Hg higher than 83% of the sediments in the

National Status and Trends data set. Our sediment samples were mostly below detection for Hg,

but one site (OCN2) within the same region sampled by Lasier et al. contained similar

concentrations of Hg as those found by that study (Appendix 4.1). Commissioner’s Creek seems

to carry Hg to the Oconee River and that Hg enters the creek after the confluence with LCC. As

with other metals, Hg readily sorbs to kaolin in freshwater (Bilinski et al. 1991). It is therefore

possible that kaolin-processing activities may be responsible for increased Hg bioaccumulation

and concentration in the fine fractions of sediment. Again, this is in accordance with the

biodynamic model of bioaccumulation (Roditi et al. 2000).

Lead (Pb)

Lead accumulation showed no significant differences in any up/downstream pair

analyzed, either using ANOVA or a linear hierarchical model. This could be due to low

bioavailability of Pb in the environment, as evidenced by how frequently we encountered tissue

samples below detection. The best fitting model for Pb used, like Hg, shell length alone. In this

case, larger individuals were found to have accumulated lower concentrations of Pb, possibly

due to growth dilution or older organisms sequestering Pb in their shells. This finding is similar

92

to previous studies that have found higher concentrations of Pb in smaller Corbicula (Abaychi

and Mustafa 1988, Angelo et al. 2007). Previous studies of kaolin-processing activity have also

not found that it substantially increases the concentrations of Pb in nearby sediments (Jordao et

al. 2002, Lasier et al. 2004).

Zinc (Zn)

ANOVA analysis showed mean tissue concentrations of Zn to be variable at five of the

six paired sites, making it the most significant differences of any element I analyzed (Figure

4.2A). The best-fit model for Zn used the variables clam condition (tissue dry mass / shell

volume) and sediment Zn concentrations (Table 4.2). Taking those variables into account, only

one pair remained significantly different (Fig. 4.3A). This pair was made up of the sites on the

Ocmulgee above and below the outfall of the former Amercord tire plant (AMC), with the

downstream site having higher concentrations. This is to be expected, since Amercord was fined

in 2003 for discharging excessive amounts of Cu and Zn compounds (ARK 2003). Increased Zn

concentrations in Corbicula tissues probably reflect this increased exposure to metals.

Two of the other site pairs which showed higher concentrations downstream were those

above and below LCC and KC. In both cases, the downstream sediment had much higher

concentrations of Zn (Appendix 4.1). While taking into account Zn sediment concentrations

rendered these differences non-significant, it should be noted that kaolin processing uses large

amounts of Zn compounds and is known to increase concentrations of Zn found in sediment

(Jordao et al. 2002, Lasier et al. 2004). Therefore, if Zn bioaccumulation in Corbicula is

strongly tied to exposure via sediment, kaolin mining and processing activities could still account

for the different Zn concentrations found at the downstream sites. This agrees with the findings

of some biogeochemical models, which are based on the idea that easily extractable metals and

93

sediment chemistry are some of the major factors in bioaccumulation by bivalves (Tessier et al.

1984).

Conclusions

Several conclusions can be drawn from the results of this study.

1) Size of individual clams can be used to predict the accumulation of many trace

elements within a site. Our selected models for all elements but As included shell length or

organism condition as a variable.

2) Location within a river system also greatly affects accumulation. Our selected models

for As, Cr and Cu all used stream distance as a variable, with Corbicula collected from upstream

sites in smaller tributaries accumulating lower concentrations. This result warns against using

smaller upstream tributaries as “reference” sites and comparing them to contaminated sites in

larger rivers. While this approach lends itself to the null hypothesis testing approach used in

ANOVA and other common statistical tests, it could produce inappropriate comparisons. In

other words, “reference” sites could show lower bioaccumulation not just because of less human

activity but also because of different chemicophysical characteristics.

3) A significant source of the potentially toxic metals Cd, Cu, Hg and Zn appears to enter

the Altamaha river system in the vicinity of the confluence of Little Commissioner’s (LCC) and

Commissioner’s Creeks. However, significant amounts of metals do not appear to be entering

the system at the confluence of Kettle (KC) and Buffalo Creeks. The evident conclusion is that

effluent from kaolin processing at LCC is increasing bioaccumulation of trace elements in the

system, while kaolin-mining activities in the watershed of KC does not seem to affect

accumulation. More studies should be conducted to determine the range and extent of kaolin

processing impacts on trace element contamination.

94

4) Our findings show agreement with some of the tenets of two of the principle models

for metal bioaccumulation in aquatic invertebrates: biogeochemical and biodynamic models.

Because bivalves both filter out particulate matter as their principle foot and interact closely with

this sediment, it is probable that both contribute to bioaccumulation of trace elements. Kaolin, as

easily suspended fine particles that readily sorb a variety of metals, may mobilize potentially

toxic trace elements as well as increasing their bioavailability. More research in this system

should be conducted to analyze the affect of particulate matter on bioaccumulation and the role

of kaolin effluent on particulate matter.

Finally, this study demonstrates the utility and necessity of taking into account both

individual and environmental differences when comparing biomonitor populations. Simply

comparing tissue concentrations between two populations can lead to erroneous conclusions and

misattribution of contamination to human activities.

Acknowledgments

I would like to acknowledge Jason Meador, Becky Fauver, Miles Buzbee, Keith Hastie

and Jeff Turner for their help in field sampling during the course of this study. I’d also like to

thank Diane Addis and Gretchen Loeffler-Peltier for help in processing samples. This study was

conducted with financial support from the US Fish & Wildlife Service as well as the University

of Georgia.

95

Table 4.1. Best fitting models for each metal, with the model’s parameters, the number of parameters (K), the residual sum of squares (RSS), the Aikake Information Criteria (AICC), difference between the AIC of the model and that of the model with the lowest AIC (Δi), and the Aikake weight (wi). Note that only models with wi within 10% of the lowest wi are included for each element (in the case of Zn there was no other model that qualified). Models selected as the final model for each element are in bold. Element Model K RSS AICc Δi wi

As Longitude, DO 5 9166 723.6 0.0 0.1269 As DO 4 9303 724.0 0.4 0.1060 As Latitude, DO 5 9186 724.0 0.4 0.1060 As Stream Distance, DO 5 9196 724.2 0.6 0.0940 Cd Thickness Index, [Cd]Sediment 7 34172 967.0 0.0 0.5433 Cd Length, % Silt/Clay 7 34342 968.0 0.9 0.3464 Cd Weight, [Cd]Sediment 7 34836 971.0 3.5 0.0944 Cr Condition, Longitude 7 4947 536.7 0.0 0.0839 Cr Longitude, Alkalinity 5 5113 537.2 0.5 0.0645 Cr Longitude 4 5189 537.3 0.6 0.0627 Cr Condition, Stream Distance 7 4967 537.3 0.6 0.0627 Cu Length, Stream Distance 7 762735 1532.64 0.0 0.3679 Cu Length, Longitude 7 765254 1533.24 0.6 0.2369 Cu Length, [Cu]Sediment 7 772011 1534.84 2.2 0.0861 Hg Length 5 689 250.6 0.0 0.5814 Hg Length, % Medium Sand 7 682 253.0 2.3 0.1282 Hg Length, Coarseness Index 7 689 254.9 4.2 0.0126 Pb Length 5 779 226.9 0.0 0.4124 Pb Dry Weight, % Silt/Clay 7 760 228.6 1.7 0.1787 Pb Height, % Silt/Clay 7 770 230.1 3.2 0.0844 Zn Condition, [Zn]Sediment 7 1452246 1649.8 0.0 0.9930

96

Table 4.2. Selected linear hierarchical models for each trace element, with the predictors for each model and percent decrease of τ00 (intrer-site variability) and σ2 (intra-site variability). The values in parentheses indicate the percentage of the total variability decreased. Element Level-2

Predictor Level-2

Predictor Level-1

Predictor % decrease in

τ00 % decrease in

σ2

As Stream Dist -0.4256

DO 0.6717

None 49.84% (19.15%)

-0.08% (-0.05%)

Cd % Silt/Clay -2.437

None Length 0.2299

-72.05% (-16.35%)

20.73% (16.01%)

Cr Stream Dist -0.7738

None Condition -0.5720

-0.30% (-0.20%)

17.79% (6.11%)

Cu Stream Dist -5.8349

None Length 11.399

27.19% (12.97%)

24.48% (12.81%)

Hg None None Length 0.2251

4.03% (3.39%)

34.66% (5.49%)

Pb None None Length -0.0017

-33.56% (-2.93%)

23.32% (18.55%)

Zn [Zn]Sediment 25.095

None Condition 3.2799

63.63% (45.41%)

11.21% (3.21%)

97

Figure 4.1. Map of the locations sampled in this study. The labels refer to the six site-pairs used to make up/downstream comparisons of trace element bioaccumulation at sites of interest: Amercord tires (AMC), Little Commissioner’s Creek (LCC), Kettle Creek (KC), Plant Hatch (PH), Rayonier (RAY), and Reidsville State Penitentiary (RSP). Figure 4.2. Average Corbicula tissue concentrations of (A) Cu and Zn; (B) As, Cd and Cr; and (C) Hg and Pb of clams found upstream and downstream of sites of interest: Amercord tires (AMC), Little Commissioner’s Creek (LCC), Kettle Creek (KC), Plant Hatch (PH), Rayonier (RAY), and Reidsville State Penitentiary (RSP). Upstream locations are in black, while downstream locations are in gray. The error bars represent standard error of concentrations. Sites with an * have significantly higher concentrations than their paired site. Significance was determined using ANOVA with Duncan’s post hoc test (α = 0.05). Figure 4.3. Differences in mean Corbicula tissue concentrations of (A) Cu and Zn; (B) As, Cd and Cr; and (C) Hg and Pb between clams found upstream and downstream of sites of interest: Amercord tires (AMC), Little Commissioner’s Creek (LCC), Kettle Creek (KC), Plant Hatch (PH), Rayonier (RAY), and Reidsville State Penitentiary (RSP). Mean Corbicula tissues at all sites had variation in each element controlled by the final model. Error bars represent 95% confidence intervals (CI95). ↑ = Upstream site has a significantly higher concentration. ↓ = Downstream site has a significantly higher concentration.

98

Figure 4.1

LCC KC

AMC

RSP

RAY

PH

99

Figure 4.2

AMC LCC KC PH RAY RSP

[Met

al] (

ppm

)

0

50

100

150

200

250Cu UpCu Dn Zn UpZn Dn

A.

*

*

*

**

*

*

AMC LCC KC PH RAY RSP

[Met

al] (

ppm

)

0

2

4

6

8

10

12As UpAs DnCd UpCd Dn Cr UpCr Dn

*

*

*

*

*

*

B.

100

Figure 4.2 (cont.)

AMC LCC KC PH RAY RSP

[Met

al] (

ppm

)

0

1

2

3

Hg UpHg DnPb UpPb Dn

C.

** *

*

101

Figure 4.3

AMC LCC KC PH RAY RSP

Diff

eren

ce in

Inte

rcep

t (pp

m)

0

10

20

30

40

50

60CuZn

A.

↑

↓

↑

↓

AMC LCC KC PH RAY RSP

Diff

eren

ce in

Inte

rcep

t (pp

m)

0

2

4

6

8

10AsCdCr

B. ↓

↓

↓

102

Figure 4.3 (cont)

AMC LCC KC PH RAY RSP

Diff

eren

ce in

Inte

rcep

ts (p

pm)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6HgPb

C.

↓

↑

103

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110

Appendix 4.1. Concentrations (ppm, μg/g) of elements at each site in three compartments: water, sediment and Corbicula tissues. Water concentrations come from a single sample, while sediments are means of 3 sub-samples from a single sample and tissue values come from whole tissue digestions of individual Corbicula (n = 1-8). OMG1 OMG2 OMG3 OMG4 LOMG OCN1 OCN2 OCN3 OCN4 COM1 COM2 COM3[As]H2O [As]Sed [As]Clam

0.6764 6.081 3.005

0.4881 BD

4.087

0.5568 BD

3.009

0.4424 4.879 3.552

0.8412 6.895 2.467

0.4227 8.269 6.220

0.2675 5.025 3.072

0.3285 10.68 4.206

0.3752 BD

3.054

0.5762 13.21 3.975

0.4903 7.348 2.126

0.4531 7.728 2.392

[Cd]H2O [Cd]Sed [Cd]Clam

0.06055 0.6118 5.145

0.02420 0.4741 2.458

0.065980.4495 2.297

0.1761 0.1919 3.049

0.033120.1397 1.521

0.071910.2276 1.457

0.02934 0.3190 5.730

0.035212.391 6.967

0.050050.1795 1.059

0.087290.0281 0.4814

0.1139 0.1185 1.833

0.1006 1.241 9.896

[Cr]H2O [Cr]Sed [Cr]Clam

1.465 67.64 2.311

0.7413 61.31 3.107

1.429 70.02 1.940

1.379 74.39 2.387

0.8700 49.63 1.569

2.182 68.93 3.098

0.9098 59.42 1.830

1.008 64.62 3.115

0.8412 62.35 1.495

2.586 66.36 1.442

2.704 62.46 1.124

1.016 81.49 BD

[Cu]H2O [Cu]Sed [Cu]Clam

2.275 31.15 54.034

1.398 22.02 64.77

2.127 22.06 34.66

2.035 21.55 38.91

1.241 12.56 108.36

2.672 45.26 60.67

1.984 26.90 58.78

1.943 32.56 47.49

1.406 22.51 43.24

2.164 25.77 24.24

1.782 29.50 31.64

2.319 24.45 60.35

[Hg]H2O [Hg]Sed [Hg]Clam

ND BD

0.2548

ND BD

0.6865

ND BD

0.2122

ND BD

0.3806

ND BD

4.341

ND BD

0.1738

ND BD

0.1750

ND 0.3088 0.2393

ND BD

0.1294

ND BD

0.0838

ND BD

0.4559

ND BD

0.9281 [Pb]H2O [Pb]Sed [Pb]Clam

0.4461 39.89 0.7593

0.5044 27.96 BD

0.8655 34.77 BD

0.2514 29.96 BD

0.7260 42.62 0.4763

0.7820 33.82 0.2423

0.2550 27.24 1.136

0.3481 29.31 0.7144

0.1705 28.53 0.2451

0.3063 24.72 0.2330

0.3644 37.13 0.4686

0.1740 35.35 0.7541

[Zn]H2O [Zn]Sed [Zn]Clam

440.7 130.2 133.2

115.1 115.0 104.1

660.0 129.6 133.5

883.2 88.92 131.4

266.2 79.18 145.7

576.3 122.9 115.9

223.4 301.2 149.2

168.0 413.1 269.6

352.2 174.4 126.4

1526 63.04 101.9

2403 97.91 128.3

1156 506.7 188.5

ND = Not detected. This element was not analyzed because of predicted low detection. BD = Below detection. All analytical values for this element from this site were below the detection limit.

111

Appendix 4.1 (cont). Concentrations (ppm, μg/g) of elements at each site in three compartments: water, sediment and Corbicula tissues. Water concentrations come from a single sample, while sediments are means of 3 sub-samples from a single sample and tissue values come from whole tissue digestions of individual Corbicula (n = 1-8).

BUF1 BUF2 BSC1 BSC2 ALT1 ALT2 ALT3 ALT4 ALT5 ALT6 OHP1 OHP2[As]H2O [As]Sed [As]Clam

0.7050 13.11 2.006

2.721 8.963 1.588

0.8807 7.984 1.711

0.6713 7.842 3.351

0.31816.481 4.363

0.4736 BD

4.677

0.4685 10.65 3.770

0.3269 8.020 5.465

0.4067 14.23 5.352

0.3081 6.902 5.195

0.5329 BD

2.335

0.82918.005 2.879

[Cd]H2O [Cd]Sed [Cd]Clam

0.04158 0.0921 1.865

0.03338 0.7487 2.218

0.022400.5718 2.902

0.043980.3358 2.183

0.13990.34122.168

0.065310.2705 2.009

0.033570.5142 2.662

0.045290.3161 4.569

0.043030.6243 3.842

0.066030.3441 1.665

0.031700.2420 2.573

0.12432.089 2.853

[Cr]H2O [Cr]Sed [Cr]Clam

0.0978 68.43 1.954

0.3508 63.35 0.934

0.2443 77.24 1.013

0.7006 73.59 1.145

1.973 70.92 1.916

1.047 56.19 8.460

0.7152 60.58 4.949

0.4709 95.23 3.685

0.7559 114.9 2.601

0.9822 69.95 5.589

0.3900 53.87 3.574

1.744 37.84 2.198

[Cu]H2O [Cu]Sed [Cu]Clam

1.164 21.82 40.87

2.387 20.90 35.88

0.4314 15.98 39.29

0.8068 14.70 55.02

2.521 25.36 49.97

1.505 19.16 43.44

1.021 21.38 53.63

33.70 18.39 36.24

2.083 26.37 64.68

1.448 23.77 67.43

0.7324 15.26 59.33

1.262 8.253 55.99

[Hg]H2O [Hg]Sed [Hg]Clam

ND 0.3998 0.9822

ND BD

0.2126

ND BD

2.037

ND BD

1.882

ND BD

0.3210

ND BD

0.3180

ND 0.3342 1.610

ND 0.3040 0.4097

ND BD

0.8811

ND BD

0.4909

ND BD

1.837

ND BD

2.316 [Pb]H2O [Pb]Sed [Pb]Clam

0.2264 23.25 0.4991

0.2848 20.99 0.3693

0.0886928.32 0.6610

0.3426 32.82 0.6059

0.640429.04 0.1495

0.2534 25.39 BD

0.1629 31.66 BD

0.3646 29.56 1.237

0.2851 34.66 0.3037

0.1631 29.47 0.2907

0.2109 39.46 1.176

0.818527.98 0.8956

[Zn]H2O [Zn]Sed [Zn]Clam

219.2 96.36 149.7

41.08 269.7 182.9

126.2 156.2 145.2

317.8 123.3 135.4

1169 164.3 137.5

235.6 137.7 137.8

447.0 181.4 136.6

155.4 109.0 121.1

306.9 113.9 122.1

466.8 169.2 112.2

106.9 96.16 162.7

496.0 62.12 147.7

ND = Not detected. This element was not analyzed because of predicted low detection. BD = Below detection. All analytical values for this element from this site were below the detection limit.

112

CHAPTER FIVE

USE OF AUTOREGRESSION TO ANALYZE LASER ABLATION INDUCTIVELY

COUPLED PLASMA-MASS SPECTROSCOPY DATA FROM BIVALVE SHELLS1

Abstract

Bivalves secrete their shells in an annual fashion, forming discrete bands of growth. In

doing so, they incorporate trace elements in concentrations based on environmental conditions

and exposure, thus making it possible to use them as archives of environmental information. In

this study, we used laser ablation inductively coupled plasma-mass spectrometry to analyze trace

elements (Cd, Cu, Mn, Pb and Zn) on a transect perpendicular to the growth annuli of the

freshwater bivalve Elliptio hopetonensis. Concentrations of Mn from multiple shells at each site

were correlated and average Mn data series were formed. Periodicity of Mn data was determined

and sampling errors removed using an autoregression model. The data series at each site were

shown to have regular fluctuations of high and low concentrations. Fluctuations were similar

between the shells from the same site but different between shells from different sites. Using Mn

correlations to offset Pb series from the same site aligns Pb peaks from multiple shells. This

study shows that Mn deposition in the shells of E. hopetonensis is a regular, seasonal process but

that growth differs between sites with different environments. It also shows that Mn series can

be used to align Pb peaks between multiple shells. By using autoregression models to remove

1 Shoults-Wilson WA, Seymour L, Unrine JM, Wiskniewski J, Black MC. To be submitted to Environmental Science and Technology.

113

sampling error, bivalve shells have the potential to be used as archives of both essential (i.e. Mn)

and potentially toxic (i.e. Pb) trace element uptake by the organism.

Introduction

Bivalve biomonitoring approaches are often employed to determine the extent and

magnitude of anthropogenic trace element release into the aquatic environment. In some places,

these programs build long term data sets that record the changes in environmental availability of

trace elements over time (Farrington 1983, Beliaeff et al. 1998, Perceval et al. 2006). In fact,

past research has indicated that long-term baseline data may be necessary for understanding the

environmental parameters affecting trace element bioaccumulation by biomonitor species

(Luoma et al. 1990). While temporally intensive studies are certainly preferable to studies

conducted on a limited time scale, the added expense from repeated sampling can make this

infeasible.

It has been shown in numerous studies that bivalves incorporate higher levels of trace

elements into their shells when exposed to higher concentrations in the laboratory (Sturresson

1976, Sturresson 1978, Imlay 1982, Bourgoin 1990, Almeida et al. 1998, Conners 1999) or in the

environment (Imlay 1982, Koide 1982, Carell et al. 1987, Schettler and Pearce 1996, Gundacker

2000, Richardson et al. 2001, Markich et al. 2002, Chiffoleau et al. 2004, Liehr et al. 2005).

Unlike tissues, trace elements deposited into the shell become locked into a matrix with much

slower turnover (Yap et al. 2003).

Furthermore, the shells of bivalves are secreted in an annual fashion, usually with distinct

bands marking periods of rapid growth (warm months) and periods of slower growth (winter

months) (Dunca et al. 2005). This means that bivalve shells can potentially serve as an archive

of trace element exposure that stretches backwards in time from the date of collection. This

114

would allow temporally intensive biomonitoring of trace metals without necessitating time

intensive sampling strategies.

Accessing these archives is dependent on understanding the complex biogeochemical

controls that influence the movement of trace elements from the environment into the shell.

These may include temperature (Stecher et al. 1996), growth (Takesue and Van Geen 2004),

dissolved oxygen (Nystrom et al. 1996), precipitation (Lazareth et al. 2003), nutrients/primary

productivity (Vander Putten et al. 2000, Langlet et al. 2007) and exposure to pollutants

(Richardson et al. 2001, Gillikin et al. 2005, Liehr et al. 2005). The trace element most

commonly investigated in freshwater bivalve shells is Mn. It has been shown to regularly peak

in the portion of annuli corresponding to early or midsummer, either due to anoxic release of Mn

from sediments (Nystrom et al. 1996) or turbation in anoxic sediments (Langlet et al. 2007) or

seasonal phytoplankton blooms (Lazareth et al. 2003).

Using laser ablation inductively coupled plasma-mass spectroscopy (LA ICP-MS), small

fluctuations in trace element concentrations in shells can be quantified (Vander Putten et al.

1999, Bellotto and Miekeley 2000, Barats et al. 2007). By using this technique to sample within

the annuli of bivalves, intra-annual (seasonal) and inter-annual (yearly) differences in trace

element accumulation can be studied. Theoretically, with enough resolution, anomalous events

(extreme weather events, pollution events, etc) recorded in bivalve shells could also be analyzed.

To date, this approach has been primarily applied to marine and estuarine bivalves, while

little work has focused on freshwater bivalves (Nystrom et al. 1996, Langlet et al. 2007). Even

fewer studies have attempted to analyze potentially toxic trace metals (e.g. Cd, Cu, Pb and Zn) in

freshwater bivalve shells (Markich et al. 2002). In this study, we analyze shells of the freshwater

mussel Elliptio hopetonensis from the Altamaha River (Georgia, USA) to 1) demonstrate the

115

utility of autoregression analysis when analyzing trace element data from bivalve shells, 2)

determine whether regular patterns of the trace element Mn can be discerned using straight-line

ablation and compared between different individuals and 3) determine whether the potentially

toxic elements Cd, Cu, Pb and Zn can be detected in the shells and related to those patterns.

Materials and Methods

Sampling

Live specimens of E. hopetonensis were collected from three sites on the lower Oconee

River (OCN) and upper Altamaha River (ALT) in southern Georgia, USA (Figure 5.1).

Locations for sampling were chosen based on previous records of E. hopetonensis populations

living along a gradient downstream of the principal source of trace elements in the watershed,

kaolin mining on the middle Oconee River (Lasier et al. 2004). Five specimens were collected at

each site and placed in a bucket of aerated site water for transport to the lab. All specimens were

measured for maximum length, height and depth before having their soft tissues removed from

the shells. The shells were then massed, labeled in pencil and dried at room temperature.

Sample Processing

The left valves of each shell were cut into several 1 mm sections along the axis of least

growth, using an Isomet® Low Speed Saw (Buehler, Lake Bluff IL, USA). This axis was chosen

to allow samples to fit on a standard petrographic slide without cutting them into segments. All

sectioning was done perpendicular to growth lines. The sections were then polished with 600,

400, 320 and 240 grit SiC and cleaned in an ultrasonic bath using 90% EtOH. One section from

each shell was mounted on a petrographic slide for LA ICP-MS analysis.

116

LA ICP-MS Analysis

The LA ICP-MS system used consists of a MerchantekTM UP frequency quintupled

Neodymium doped Yttrium Aluminum Garnet (Nd-YAG) operating at 213 nm, coupled to a

Perkin-Elmer Sciex Elan DRC Plus ICP-MS (Norwalk CT, USA). Ablations were made in a

straight line as close to orthogonal to growth lines as possible. A pre-ablation pass to remove

surface contamination [as recommended by Vander Putten et al. (1999)] was performed at a spot

size of 75 μm, a repetition rate of 20 Hz and a scanning speed of 25 μm/s. The second, analytical

pass was made with similar operating parameters but at a slower speed of 5 μm/s. In some cases,

a single line was ablated, while in larger shells, a second line was ablated at a different angle in

order to account for curvature in the shell. Isotopes that were analyzed in this manner were

55Mn, 65Cu, 68Zn, 114Cd, and 208Pb. Data were calibrated using a series of calcium carbonate

standards prepared in house and having concentrations ranging from 0 to 100 ppm for all

elements analyzed [as recommended by Bellotto and Miekeley (2000)]. Standards were

analyzed along a 900 μm transect for a total of 80 values per calibration. To allow comparison

between standards and samples, all values were calibrated using Ca43 as an internal standard

(Vander Putten et al. 1999).

Statistical Analysis

Different trace metals within the same shell were correlated with each other using Excel

(Microsoft, Redmond WA, USA), in order to determine if there was any direct relation between

accumulation of different elements within the shell. The element Mn was chosen to construct

models for all shells because past studies (Nystrom et al. 1996, Langlet et al. 2007) and our own

preliminary study (data not shown) indicated the regular, seasonal nature of its incorporation into

117

freshwater bivalve shells. The data series for Mn that we collected also showed some of the

strongest correlations between shells at the same site, which made them amenable to

combination (see below). Preliminary studies (data not shown) indicated that trace element data

collected from bivalve shells is highly autocorrelated—data points are correlated to earlier data

points in the same series. In order to account for autocorrelation, we treated the data as a time

series, incorporating “seasonal” (cyclical) data into an autoregression model that removed errors

caused by autocorrelated data (Chatfield 2004).

The first step in model building was to create mean Mn data series for each site.

Observations of Mn concentration in shells from the same sites were correlated to one another at

different lags (differences in observation number). Best-fit lags were determined based on: 1)

minimizing the lags between shells; 2) obtaining correlations above 0.250 between as many

shells as possible; 3) visual inspection of the mean time series to determine that the number of

peaks have not greatly increased nor decreased when compared to the Mn data series of

individual shells. For values that were at the upper limit of detection, Mn concentrations as a

function of Ca were calculated for each shell and a standard value was substituted for above

detection observations. This was only extensively used for shells from the site OCN1 (Fig 5.1).

Average Mn data series were then examined for cyclical patterns in concentration. In

cases where more than one ablation line was used, these data series were separated and analyzed

individually. After estimation of peak distances, the linear autocorrelation function of each data

series was determined using an autoregressive integrated moving average (ARIMA) procedure in

Statistical Analysis Software (SAS Institute, Cary NC, USA ). This procedure measures

correlation between points within the same data series at different distances apart within the

series (e.g. different lags). Autocorrelation data was plotted at lags up to 360, with correlations >

118

|2*Standard Error| being considered significant. Lags that were indicated to have significant

correlation were introduced into the data series in the form of a “cycle” value (i.e. for a lag of

three, observations 1-3 would be assigned a cycle parameter of 1-3, but observation 4 would be

assigned a 1, observation 5 a 2, observation 6 a 3, observation 7 at 1, etc). The mean values of

all observations with the same cycle value then became separate parameters of the regression.

Bayes Information Criteria were calculated for all models to determine the appropriate

lag for autoregression. Autoregression takes into account errors caused by data series whose

values are autocorrelated. Following autoregression at the appropriate lag, the p-values of all

cyclical parameters were assessed for significance. The residuals of the autoregression models

were assessed for normality using the Kolmogorov-Smirnov test (α = 0.05) with no more than

1.5% of residuals removed as outliers. Lingering correlation was assessed using the

autocorrelation function as described above. Models were determined to be appropriate for use

if the mean square error (MSE) was less than that of the least squares (linear, non-cyclical)

autoregression model and the residuals were considered normal and uncorrelated. The model

with the lowest MSE was then chosen as the final autoregression model.

Finally, the lags used to construct the mean Mn data series were applied to trace metals

Cd, Cu, Pb and Zn and correlation between shells analyzed, in order to determine if the Mn data

series could be used to match up peaks of potentially toxic element accumulation within the

shell.

Results

General parameters for each shell are collected in Table 5.1. The site OCN1 consisted of

older, larger individuals with thicker shells, while OCN2 was a population of smaller, younger

individuals.

119

The correlation between different elements within each shell are listed in Appendix 5.1.

Elements rarely showed correlation with another element. However, Mn concentrations were

consistently correlated with Zn concentration, although this correlation was in some cases

negative and in others positive (Appendix 5.1). This is probably because Zn behaved differently

from Cd, Cu and Pb, declining in a linear or exponential manner with occasional broad peaks as

the ablation moved away from the growth edge of the shell. The elements Cd, Cu and Pb were

characterized by a flat baseline with occasional, sharp peaks. Also, concentrations of Cd showed

occasional correlation to Pb concentrations.

Graphs of the individual Mn data series, grouped by site and offset by lags of greatest

collective correlation, and the mean Mn data series are displayed in Figure 5.2. The correlations

between Mn from different shells at the chosen lags are listed in Table 5.2. Only one correlation

between shells at the same site was less than |0.25| (shells M1 and M2 from OCN2).

The fit of the least squares regression are given in Table 5.3. Notice that for the sites

OCN1 and ALT1, two least squares regressions are given, which represent the two separate

ablations used to follow the curvature of the shell. For most of the data series, the concentration

of Mn generally decreased as the ablations moved towards the umbo (i.e. towards the older parts

of the shell).

The final models determined for each data series are described in Table 5.4. All data

series include a cyclical component which, when included in the autoregression, decreased the

MSE as compared to the linear least squares autoregression (Table 5.4). In the case of the first

ablation of OCN1, this cyclical component was relatively weak, with none of the cyclical

parameters being significant (α = 0.10). This is probably because most of the data in this range

was above the upper limit of detection for all four shells and was substituted with standard

120

values. The data series and their linear regressions with cyclical components are compared in

Figure 5.3. Cyclical components do not match exactly with all peaks in a given data series,

being selected based on reducing MSE.

There were no strong correlations between Cd, Cu, or Pb from different shells at the lags

used to create the average Mn series (Appendix 5.2) or at any other lags (data not shown). This

is probably because of the irregular, “spiky” nature of the data. Zn did show correlation between

different shells but often showed higher correlation at lags other than those that were used for the

average Mn series. However, visual inspection of Pb data from the shells at site OCN2 indicate

that lags used for the average Mn data series aligned peaks from multiple shells, as shown in

Figure 5.4. Shells from OCN1 also exhibited some alignment of Pb peaks (Figure 5.4). Only

shells from ALT1 did not show this trend, probably due to the fact that only one shell from that

site showed discernible Pb peaks. Visual inspection of Cd and Cu did not reveal any obvious

peak alignment as was the case for Pb.

Discussion

Utility of Autoregression Models

This is the first study that the authors are aware of in which trace element data from

bivalve shell annuli were analyzed using an autoregression function. This is surprising,

considering that several studies have concluded that trace elements in bivalve shells are linked to

regular, seasonal cycles of temperature, growth, precipitation, primary productivity or some

combination (Nystrom et al. 1996, Vander Putten et al. 2000, Lazareth et al. 2003, Freitas et al.

2006, Langlet et al. 2007). Seasonal parameters such as these fluctuate in an autocorrelated

manner: considering a measurement of such a parameter independent from previous

measurements introduces errors. Because bivalves secrete shell in a continuous manner,

121

dependent on environmental parameters such as temperature and pollution levels (Dunca et al.

2005), statistical treatment of data from shell annuli must use statistical methods that removes

autocorrelation errors.

Strong autocorrelation was found in all data series of Mn in our study. As shown by

Table 4, a model that took autocorrelation into account fit the date much better than a linear least

squares model for each data series. This clearly demonstrates the utility of autoregression when

analyzing trace metal concentrations collected from bivalve shells. Our study also found that, on

the whole, Mn concentrations declined as the ablation continued into the older annuli of the

shell. This has previously been reported in the literature and has been explained as an

ontogenetic effect of the mussel’s age affecting shell formation (Imlay 1982).

Seasonality of Mn Incorporation in Bivalve Shells

Our study also shows that the freshwater bivalve E. hopetonensis incorporates Mn into its

shell in a cyclical fashion. The size of these cycles (number of data points contained in one

cycle) differed between data series that came from different angles of ablation (if more than one

was used) or different sites (Table 5.4). However, it seems that cycle size is conserved between

shells originating from the same site, as demonstrated by the between-shell correlations in Table

5.2. This could indicate that local environmental factors affect the shell growth and Mn

incorporation of individuals.

Our study was also the first to determine the strength of observed seasonal trends. As

demonstrated by Table 5.4, some cyclical trends were stronger than others, with significant

components ranging from 0-71.93%. It could be argued that the weakness of the cyclic

parameters for the first ablation line of shells from OCN1 indicates that the cyclical model is

inappropriate. However, this weakness is probably due to the fact that many points in these

122

shells were at the upper limit of detection and were replaced by a standard value, an approach

known to cause distortions (Park et al. 2007).

With regards to whether Mn concentration in bivalve shells fluctuates in a seasonal

manner, our method of data collection does not allow us to directly determine where individual

peaks occur within a growth annuli. However, our estimates for the number of annuli that are

ablated (Table 5.1) agree to some degree with the number of peaks found in each mean Mn

series. For instance, the series for OCN2 shows 4 peaks (Figure 5.4), while 2-3 annuli were

ablated in each shell. The series for ALT1 shows 15 peaks from shells that had an average of 8

annuli ablated. However, peaks from the first ablation seem to represent a pattern of large and

small peaks that may indicate some kind of biannual trend. In this case, the data represents 7-8

seasons, which agrees with annuli counts. Because of values that were uniformly above

detection, it is difficult to count peaks in OCN1. Overall, comparing mean Mn peaks to annuli

counts agrees with other studies that have found Mn accumulation to show a strictly seasonal

trend (Nystrom et al. 1996, Vander Putten et al. 2000, Siegele et al. 2001, Freitas et al. 2006,

Langlet et al. 2007).

Past researchers have suggested that increased Mn concentrations in bivalve shells could

be the result of increased summer concentrations of Mn caused by anoxic waters (Nystrom et al.

1996), early summer increases in primary productivity (Vander Putten et al. 2000, Lazareth et al.

2003) or a combination of the two (Langlet et al. 2007). While we do not have environmental

monitoring data that could allow us to test either of these hypotheses, we can make a few

observations, the shells from OCN1 which routinely had Mn concentrations above the

instrumental detection were collected from a small slack water on the bankward side of a

sandbar. Periodic disconnection at this site could result in slower moving, more anoxic water in

123

the summer than at OCN2 (undercut bank with strong current) or ALT (fast flowing and sandy).

Data collected by USGS at sites (Doctortown and Everett GA) downstream of those sampled in

this study indicate average water concentrations of Mn to be higher in summer months (July and

August), while dissolved oxygen is lower (USGS 2008a, b). Because Mn is associated with

oxides, it is readily mobilized under anoxic conditions (Koretsky et al. 2006, Poykio et al. 2007).

Therefore, it is plausible that later summer anoxia leads to the seasonal increases in Mn

concentrations that we found in the shells of E. hopetonensis.

Bivalve Shells as Long-Term Records of Trace Element Pollution

Past studies have shown that higher concentrations of Cd, Cu, Pb and Zn in the annuli of

bivalve shells can be linked to higher concentrations of those trace elements in the environment

(Schettler and Pearce 1996, Richardson et al. 2001, Markich et al. 2002, Liehr et al. 2005).

However, past attempts to use annuli as archives of past contamination have given mixed results.

Markich et al. (2002) analyzed the trace elements in the freshwater species, Velesunio angasi, at

sites downstream of a mine waste site that had recently been remediated. They found annuli laid

down after remediation had significantly lower concentrations of Cu and Zn than annuli laid

down prior to remediation. Gillikin et al. (2005) constructed a 53-year series of Pb found in the

shells of Mercenaria mercenaria from a coastal island. They found that Pb concentrations in

annuli corresponding to the years 1977-1981 were significantly higher than years before and

after that time period. They found no other trends, not even seasonal trends in Pb seen by other

researchers (Vander Putten et al. 2000, Freitas et al. 2006).

Our specimens showed very low concentrations of the elements Cd, Cu and Pb, with only

a few individuals showing discernable peaks in Pb concentration (Figure 5.4). This could be a

function of the species, E. hopetonensis, being less likely to incorporate these elements into its

124

shell than other species that have been studied. Or it could be the result of low environmental

concentrations of the elements in the system, since the Lower Oconee/Upper Altamaha has little

urbanization and only a few significant local sources of potentially toxic elements [see Chapter

4; (Lasier et al. 2004)]. Our findings did indicate that aligning Pb data series from different

shells based on the lags of best agreement for Mn data series seems to match peaks between

shells (Figure 5.4A,B). This is especially obvious when looking at the younger shells from

OCN2 (Figure 5.4B), where an increase in observed Pb concentrations in 3-4 of the 4 shells

accompanies the mean series’ most prominent peak in Pb concentrations. This concurrence

indicates that the seasonal trends seen in Mn accumulation could be used to match up many

elemental data series between shells. By doing this, periods of increased Pb accumulation are

also aligned. As seasonal Mn incorporation into bivalve shells becomes better understood, it

should be used to determine dates of increased accumulation of elements such as Pb.

Future Directions

This study illustrates one of the weaknesses in using straight-line rather than spot ablation

when analyzing bivalve shells. Unlike spot ablation, data points collected using straight-line

ablation cannot be easily related to a specific portion of shell annuli. However, straight-line

ablation provides a higher degree of resolution, and is more capable of detecting small

fluctuations in trace element concentration that could indicate temporary exposure to increased

concentrations of trace elements.

This is one of several issues to be addressed by future analysis of the data generated in

this study and in future studies. Firstly, a means of directly linking trace element data that

demonstrates a seasonal nature to dates as explained by annual growth rings would allow

anomalies to be related to environmental disturbances or, potentially, pollution events.

125

Secondly, a more robust model must be developed that allows for differences in individual

shells, rather than smoothing them out by averaging them together. Such a model would be able

to take into account above detection values and changes in the angle of ablation. It could also

elucidate the differences not just between individuals at a single site, but between sites. Thirdly,

having developed a new, more robust approach, it should further be applied to other trace

elements such as barium (Ba) and strontium (Sr), which were not considered in this study.

Conclusions

This study has shown that straight-line laser ablation can be used to reveal regular

patterns in Mn concentration in the shells of the freshwater bivalve E. hopetonensis. The data

series generated by this study can be well correlated between individuals from the same site and

in doing so, the data series of the potentially toxic trace element Pb are aligned with one another.

Furthermore, the necessity of taking into account autocorrelation when analyzing data generated

from bivalve shells was demonstrated by the fit of autoregression when compared to that of least

squares linear regression. The shells of freshwater bivalves continue to be a promising source of

archival data of trace element pollution in a given region. As we better understand the links

between the accumulation of elements such as Ba, Mn and Sr in bivalve shells and seasonal,

environmental parameters, we can better understand the factors behind accumulation of

potentially toxic trace elements such as Cd, Cu, Pb and Zn. Understanding these relationships

will require a combination of controlled laboratory experiments, caging and field studies under a

variety of conditions. It will also require the development of robust statistical models that can

account for autocorrelation errors, the environmental differences between different sites and the

growth history of individuals.

126

Acknowledgments

I would like to acknowledge Jason Meador and Miles Buzbee for their help in field

sampling during the course of this study. Thanks are also due to Monica Carroll for her advice

on sample processing and analysis. This study was conducted with financial support from the

US Fish & Wildlife Service as well as the University of Georgia.

127

Table 5.1. Mean concentrations of elements (μg/g of Ca43), size parameters (shell length, height, depth and shell weight) and estimated number of annuli ablated (i.e. number of years the trace element data represents) of individual shells analyzed in this study. Average values for each site are given in bold.

Site Shell Length (mm)

Height (mm)

Depth (mm)

Shell Wt. (g)

Annuli Ablated

[Cd/Ca] (μg/g)

[Cu/Ca] (μg/g)

[Mn/Ca] (μg/g)

[Pb/Ca] (μg/g)

[Zn/Ca] (μg/g)

M1 105.5 63.5 35.0 111.55 15 4.390 2.481 351.3 2.821 14.98 M2 108.9 56.4 31.2 86.72 18 4.392 2.492 445.1 2.821 17.99 M3 114.2 61.4 34.3 113.68 7 4.407 2.506 412.1 2.848 10.06 M4 121.4 66.0 35.3 118.34 19 4.393 2.476 415.0 2.849 16.30

OCN1

AVG 112.5 61.8 34.0 107.57 14.75 4.396 2.489 405.9 2.835 14.83 M1 92.0 43.2 24.9 29.01 3 4.396 2.492 137.1 2.833 12.25 M2 90.1 49.0 23.5 27.60 2 4.387 2.472 126.2 2.846 8.70 M3 90.5 41.1 19.4 19.91 2 4.394 2.463 167.0 2.835 13.47 M4 88.6 41.6 19.9 25.54 3 4.383 2.425 134.9 2.816 1.23

OCN2

AVG 90.3 43.7 21.9 25.51 2.5 4.390 2.463 141.3 2.833 8.91 M1 106.8 50.8 29.0 57.64 9 4.440 2.566 227.1 3.430 4.86 M2 102.8 51.4 31.0 26.19 10 4.395 2.471 177.9 2.841 7.90 M3 92.6 49.7 25.7 50.63 5 4.382 2.443 153.2 2.815 10.60

ALT1

AVG 100.7 50.6 28.6 44.82 8 4.406 2.493 186.1 3.029 7.79

128

Table 5.2. Correlations between Mn data series for shells within a given site, at the lags selected for greatest average correlation. Values in bold are greater than 0.25 or less than –0.25. Correlations between the same data sets = 1.000.

Site Shell M1 M2 M3 M4 M1 1.000 M2 0.431 1.000 M3 0.266 0.647 1.000

OCN1

M4 0.363 0.379 0.351 1.000 M1 1.000 M2 -0.013 1.000 M3 0.601 0.316 1.000

OCN2

M4 0.332 0.401 0.251 1.000 M1 1.000 M2 0.472 1.000

ALT1

M3 0.505 0.527 1.000

129

Table 5.3. The fit of the least squares lines for the Mn data series, calculated for a linear regression (Lin) and a linear autoreggression (Auto).

Site

Data Points

Intercept (Lin)

Slope (Lin)

r2 (Lin)

Intercept(Auto)

Slope (Auto)

r2 (Auto)

1-950 484.05 -0.046 0.086 474.16 -0.028 0.918 OCN1 951-2073 441.37 -0.184 0.634 452.06 -0.208 0.989

OCN2 1-1253 114.88 0.043 0.243 125.71 0.027 0.993 1-538 284.44 -0.145 0.171 283.94 -0.149 0.994 ALT1

539-1099 124.42 0.026 0.067 126.16 0.012 0.984

130

Table 5.4. Autoregression models for Mn data series, including number of cyclical components, % of cyclical components that are significant (α = 0.10) and MSE for both the least squares (LS) model and the least squares model with cyclical components (LSCC). Site Data

Points Cyclical

Components % Significant Components

MSE (LS)

MSE (LSCC)

OCN1 1-950 91 0.00% 154.05 149.79 951-2073 292 37.33% 63.066 59.897

OCN2 1-1253 285 71.93% 7.0953 6.8595 ALT1 1-538 49 57.14% 18.628 17.906

538-1098 126 30.95% 4.1078 3.8545

131

Figure 5.1. Sampling locations on the Oconee (OCN) and Altamaha (ALT) Rivers. Figure 5.2. Mn data series, offset by lags of highest overall concentration. The gray lines are individual shells, while the black line is the mean series. Vertical lines indicate the end of the first ablation line. The individual plots represent the three sites OCN1, OCN2, and ALT1 in a downstream manner. Figure 5.3. Regressions (black lines) of mean Mn series (gray lines). The regressions include cyclical components as described in Table 5. The individual plots represent the three sites OCN1, OCN2, and ALT1 in a downstream manner. Figure 5.4. Mean Pb series (black line), offset by the same lags as the mean Mn series. The bars at the bottom indicate the number of shells with above average Pb values at each point on the series. Gray boxes highlight peaks in the mean Pb series that coincide with above average values in several shells. The individual plots represent the three sites OCN1, OCN2, and ALT1 in a downstream manner.

132

Figure 5.1

OCN1

OCN2

ALT1

133

Figure 5.2.

Observations

0 500 1000 1500 2000

[Mn/

Ca]

( μg/

g)

0

100

200

300

400

500

600

700OCN1

Observations

0 200 400 600 800 1000 1200 1400 1600

[Mn/

Ca]

( μg/

g)

0

50

100

150

200

250

300

350

400OCN2

134

Figure 5.2 (cont).

Observations

0 200 400 600 800 1000 1200

[Mn/

Ca]

( μg/

g)

0

100

200

300

400

500

600ALT1

135

Figure 5.3.

Observations

0 500 1000 1500 2000

[Mn/

Ca]

( μg/

g)

100

200

300

400

500

600OCN1

Observations

0 200 400 600 800 1000 1200

[Mn/

Ca]

( μg/

g)

80

100

120

140

160

180

200

220

240OCN2

136

Figure 5.3 (cont).

Observations

0 200 400 600 800 1000

[Mn/

Ca]

( μg/

g)

50

100

150

200

250

300

350

400ALT1

137

Figure 5.4.

Observations

0 500 1000 1500 2000

[Pb/

Ca]

( μg/

g)

2.8

3.0

3.2

3.6

OCN1

Observations

0 200 400 600 800 1000 1200

[Pb/

Ca]

( μg/

g)

3.0

3.5

5.0

OCN2

138

Figure 5.4 (cont).

Observations

0 200 400 600 800 1000

[Pb/

Ca]

( μg/

g)

10

20

45

60

ALT1

139

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Appendix 5.1 Correlations between different elements within the same shell. Values in bold are greater than 0.25 or less than –0.25.

Site Shell Mn/Cd Mn/Cu Mn/Pb Mn/Zn Cd/Cu Cd/Pb Cd/Zn Cu/Pb Cu/Zn Pb/Zn M1 0.0457 0.0425 0.0592 0.2942 0.0076 0.0797 -0.0091 -0.0117 0.0547 0.0691 M2 -0.0417 0.0107 0.0047 0.7024 -0.0177 0.0084 -0.0435 0.0037 0.0457 0.0219 M3 -0.0074 -0.0782 -0.1175 0.2646 0.0182 0.1652 0.0319 0.1586 0.0843 0.0252

OCN1

M4 0.0068 -0.0966 0.0161 0.5516 -0.0083 0.2048 -0.0319 0.0006 -0.0473 -0.1409 M1 0.0445 -0.0106 0.2142 -0.1778 -0.0119 0.1582 -0.0485 0.0192 0.0753 -0.1340 M2 0.0030 0.0223 -0.0228 -0.3901 0.0360 0.2544 -0.0147 -0.0129 0.0368 -0.0206 M3 0.0364 0.0972 0.0097 -0.5749 0.2450 0.0367 -0.0493 -0.0361 -0.1051 0.0496

OCN2

M4 0.0482 -0.0139 0.0657 0.3735 -0.0153 0.4681 0.1361 -0.0373 -0.0245 0.0963 M1 0.0867 0.0635 0.0771 0.5557 0.0004 0.9010 0.0136 0.0010 -0.0008 0.0073 M2 0.0345 -0.0973 -0.0492 0.6134 0.1091 0.1851 0.0864 -0.0222 -0.0091 -0.0492

ALT

M3 -0.0281 -0.0252 0.0401 0.6292 -0.0160 -0.0004 -0.0446 0.0054 -0.0090 0.0542

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Appendix 5.2. The correlation between Cd, Cu, Pb and Zn from different shells at the lags determined by correlations between Mn data series. Values in bold are greater than 0.25 or less than –0.25. Correlations between the same data sets = 1.000.

Cd Cu Pb Zn Site Shell M1 M2 M3 M4 M1 M2 M3 M4 M1 M2 M3 M4 M1 M2 M3 M4

M1 1.000 1.000 1.000 1.000 M2 0.016 1.000 -0.021 1.000 -0.007 1.000 0.730 1.000 M3 0.066 0.010 1.000 -0.007 -0.022 1.000 -0.021 -0.008 1.000 -0.001 0.139 1.000

OCN1

M4 -0.006 -0.008 0.003 1.000 -0.017 -0.011 -0.008 1.000 -0.011 -0.008 0.002 1.000 0.735 0.741 0.025 1.000 M1 1.000 1.000 1.000 1.000 M2 -0.008 1.000 0.016 1.000 0.025 1.000 0.862 1.000 M3 -0.002 -0.008 1.000 -0.052 0.023 1.000 0.034 -0.004 1.000 -0.405 0.602 1.000

OCN2

M4 0.011 0.020 -0.018 1.000 -0.013 -0.005 0.010 1.000 0.016 0.004 -0.002 1.000 -0.628 -0.530 0.604 1.000 M1 1.000 1.000 1.000 1.000 M2 0.008 1.000 -0.006 1.000 -0.005 1.000 0.803 1.000

ALT1

M3 0.002 -0.012 1.000 -0.031 -0.013 1.000 -0.012 -0.028 1.000 0.803 0.912 1.000

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CHAPTER SIX

CONCLUDING REMARKS

This dissertation has attempted to address a number of the devilish details that can hinder

efforts to monitor potentially toxic trace element contamination in lotic systems. The first goal

was to determine whether bioaccumulation of trace elements in the tissues of the Asian clam

Corbicula fluminea accurately reflected the accumulation in the tissues of a co-occurring native

mussel species (Elliptio hopetonensis) at the same sites. While this seems like an obvious mode

of inquiry, there is a sad lack of direct comparisons in the literature between freshwater mussels

and the invasive species that are assumed to represent them as biomonitors. This dissertation

determined that several trace elements (As, Cd, Cu, Hg, Pb and Zn) showed similar trends

between each bivalve species. However, the study revealed that a number of other factors such

as size, season of collection (represented by temperature and to some extent dissolved oxygen)

and water chemistry may be in some way influencing accumulation. Without taking into account

these factors, it would be difficult to draw firm conclusions about the cause of increased

accumulation of trace elements found at any given site.

The next step, then, was to expand the spatial range of the study and the number of

environmental parameters that were analyzed along with the trace elements. The key to

implementing this step was also in employing hierarchical linear models as a means of

accounting for individual variation in trace element accumulation related to growth as well as

site-specific variation related to location within the watershed and local water and sediment

characteristics. That hydrological, chemical, physical and biological parameters should affect

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bioaccumulation should come as no surprise. What is needed in biomonitoring efforts such as

this one is not just the recognition that those parameters affect the outcome but an attempt to

remove naturally occurring variation. In other words, more sophisticated and flexible statistical

models than the typical null hypothesis testing approaches need to be employed. By removing

uncertainty caused by naturally occurring variation, biomonitoring approaches can truly achieve

their ostensible goal of identifying the presence and impacts of anthropogenic pollution.

In its final study, I attempted to use freshwater bivalve shells as archives of exposure to

trace elements, as well as a record of seasonal environmental processes. The results lend

credence to the idea that trace element accumulation in annuli of bivalve shells can eventually be

tapped as a source of environmental data. Bivalve shells may be recording information on

climate, hydrology, water chemistry and pollution events within their shells. It will require an

array of validating studies as well as specialized statistical models to access this data and fully

understand its implications. Because bivalve shells are secreted in a sequential manner, their

trace element concentrations are autocorrelated and again, cannot be dealt with using null

hypothesis testing. Despite these challenges, the benefits of using shells as environmental

archives is clear, with implications including bivalve aging (the current techniques of which are

in dispute), monitoring the effects of climate change as they directly impact an organism and

possibly, retroactive monitoring of pollution events.

Overall, this dissertation makes the case for a carefully considered, appropriately

analyzed biomonitoring protocol when investigating trace elements. Naturally occurring

variation in bioaccumulation needs to be controlled for accurate conclusions to be drawn.

Potential biomonitor organisms need to meet a variety of criteria, including validation of their

suitability that takes place under the variable conditions that biomonitor programs would expect

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to encounter in the field. At the same time, biomonitoring programs need to keep in mind their

ultimate goals, as in these studies, which attempted to identify sources of potentially toxic trace

elements to threatened native bivalves.

The mussels of the Altamaha River system are a highly unique and in some instances

imperiled group of organisms that inhabit a large, relatively unaltered drainage. Concurrent to

the studies reported on here, other researchers were investigating the genetics of Altamaha

mussels, their populations and our ability to accurately assess them and the movement of organic

xenobiotics within the Altamaha and its tributaries. At the same time, the Altamaha

spinymussel, Elliptio spinosa, was finally slated for federal listing as an endangered species,

underscoring the necessity of conservation and monitoring of this system. And so the work

continues, with a concerted effort to determine fish hosts for all the endemic species of the

Altamaha. Between the efforts of researchers to understand the unique mussel populations and

the stressors that they face and the actions of conservation-minded citizens groups such as the

Altamaha Riverkeeper, it is entirely possible that mussels and their habitat, will be preserved.